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A Qualitative Study That Explores the Implementation of Artificial Intelligence in Integrated
Circuit Design
A Dissertation Presented in Partial Fulfillment of the
Requirements for the Degree of
Doctor of Computer Science
By
Danny Rittman
Department of Doctoral Studies, Colorado Technical University
March 2023
Committee Members
Alexa Schmitt, PhD, Chair
Abdullah Alshboul, DBA, Committee Member
Jeffrey Butler, PhD, Committee Member
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Abstract
The development of data processing technology relies heavily on integrated circuits
(ICs), which are electronic components that perform various tasks, including complex
computations. Designing and manufacturing these ICs is a challenging and expensive process, as
the pursuit of higher performance, miniaturization, and energy efficiency drives the industry
forward. While artificial intelligence (AI) is expected to handle the task, technological limitations
persist. This qualitative research purpose is to investigate the gap between AI applications and
IC design by exploring available options. The study adopts a qualitative approach based on the
interpretivism paradigm and involves interviews with eight industry professionals with
extensive expertise and experience in AI and integrated circuits. Journal articles on AI and IC
design were also consulted to supplement the participants' insights. Evidence suggests that AI is
revolutionizing the computing industry using advanced ICs. Future microchip development will
continue to incorporate more AI systems and methods as a core part of design automation. The
integration of AI is expected to bring various benefits, such as the efficient automation of
repetitive tasks, handling large IC design and verification processes, addressing complex
computational tasks, and exploring new possibilities. The study's findings emphasize the
immense potential of AI technology to propel the IC design industry forward, allowing for
advanced microchip design and unlocking numerous possibilities for future technological
advancements.
Keywords: Artificial intelligence, integrated circuits design, IC verification, Computing
power.
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Acknowledgments
I would like to begin by expressing my heartfelt appreciation to my advisor, Dr. Alexa
Schmitt. Throughout the dissertation process, she provided me with valuable feedback,
support, and advice on important aspects of the study. I want to express my sincere
appreciation to Dr. Jeffrey Butler and Dr. Abdullah Alshboul, who served as members of my
thesis committee. Their guidance and feedback on the work presented in this study have been
invaluable.
I am grateful to my family for their unwavering encouragement and support throughout
my academic journey.
A sincere thank you goes to my childhood friend, Mo. Your enduring friendship and
unwavering support have been a driving force throughout this dissertation journey. From
shared childhood memories to shared aspirations, your presence has been invaluable. Thank
you, Mo, for being an essential part of my journey and accomplishments.
Additionally, I extend my thanks to my colleagues, whose feedback on the course
platforms helped to refine the paper. I am also grateful to all the participants who shared their
knowledge for the study and played a pivotal role in the content's development thus far.
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Table of Contents
CHAPTER 1: INTRODUCTION ................................................................................................................ 1
STUDY PROBLEM .......................................................................................................................................... 5
STUDY PURPOSE .......................................................................................................................................... 7
RESEARCH QUESTION.................................................................................................................................... 9
CONCEPTUAL FRAMEWORK ........................................................................................................................... 9
SIGNIFICANCE OF THE STUDY ....................................................................................................................... 14
RESEARCHER POSITIONALITY AND REFLEXIVITY ............................................................................................. 15
DELIMITATIONS AND LIMITATIONS ............................................................................................................... 15
DEFINITION OF TERMS ................................................................................................................................ 16
CHAPTER SUMMARY ................................................................................................................................... 17
CHAPTER 2: REVIEW OF THE LITERATURE ........................................................................................ 19
LITERATURE SEARCH STRATEGIES ................................................................................................................. 20
ARTIFICIAL INTELLIGENCE (AI) ..................................................................................................................... 20
AI DEVICES ................................................................................................................................................. 24
INTEGRATED CIRCUITS ................................................................................................................................. 25
ARTIFICIAL INTELLIGENCE AND INTEGRATED CIRCUITS DESIGN....................................................................... 27
CIRCUIT VERIFICATION ................................................................................................................................ 35
GAPS IN THE LITERATURE ............................................................................................................................ 41
CONCLUSIONS ............................................................................................................................................ 45
CHAPTER SUMMARY ................................................................................................................................... 47
CHAPTER 3: METHODOLOGY, DESIGN AND METHODS ................................................................... 49
RESEARCH METHODOLOGY AND DESIGN...................................................................................................... 49
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POPULATION, SAMPLE, AND PARTICIPANT RECRUITMENT ............................................................................. 51
DATA COLLECTION INSTRUMENTATION AND PROCEDURES ............................................................................ 52
DATA ANALYSIS PROCEDURES ..................................................................................................................... 54
TRUSTWORTHINESS .................................................................................................................................... 55
ETHICAL ASSURANCES ................................................................................................................................. 57
CHAPTER SUMMARY ................................................................................................................................... 58
CHAPTER 4: FINDINGS ......................................................................................................................... 59
DESCRIPTION OF THE STUDY SAMPLE ........................................................................................................... 59
RESULTS ..................................................................................................................................................... 61
DISCUSSION OF STUDY FINDINGS ................................................................................................................. 70
CHAPTER SUMMARY ................................................................................................................................... 71
CHAPTER 5: DISCUSSION AND CONCLUSIONS ................................................................................. 72
LIMITATIONS OF STUDY FINDINGS ................................................................................................................ 72
INTERPRETATION OF STUDY FINDINGS.......................................................................................................... 73
PRACTICE IMPLICATIONS OF STUDY FINDINGS .............................................................................................. 75
RESEARCHER REFLECTIONS .......................................................................................................................... 76
RECOMMENDATIONS FOR FURTHER RESEARCH ............................................................................................ 77
CONCLUSION .............................................................................................................................................. 78
REFERENCES ......................................................................................................................................... 82
APPENDIX A: QUESTIONNAIRE ........................................................................................................ 103
DEMOGRAPHICS ....................................................................................................................................... 103
AI & IC DESIGN ........................................................................................................................................ 103
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LIST OF FIGURES
Figure 1: Conceptual Framework ...................................................................................... 14
Figure 2: Question 11 results analysis .............................................................................. 66
Figure 3: Question 12, 14, 15 results analysis……………………………………………………………….67
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Chapter 1: Introduction
Technology is an ever-changing field that seeks to solve complex problems with
machines and computers (Liu et al., 2016). While the invention of computers kick-started the
era of the modern technological world, there are still areas that are moving much slower.
Advances such as artificial intelligence (AI), machine learning (ML), and powerful computing
devices, have altered the handling of different activities. Technological solutions currently cover
almost all areas, with a unique adaptation of computational solutions in each field. The
expectation is that technology will keep on improving problem-solving, with some ventures
seeking more efficient machine coordination in the solutions. The mentioned advances in
technology all rely on core components called integrated circuits (IC), which perform a vast
number of tasks and computations. These items have been adapted and implemented in many
devices, and they are becoming increasingly powerful (Matsui et al., 2019).
Integrated circuit design is a field that handles the creation of ICs, which make up many
different parts of computers (Kaur & Gill, 2016). An integrated circuit (IC), also known as a
microchip, is a miniaturized electronic circuit that contains a large number of interconnected
electronic components, such as transistors, resistors, capacitors, and diodes, on a single
semiconductor wafer or chip. The components are etched onto the chip's surface using various
semiconductor manufacturing techniques, allowing for the integration of complex
functionalities into a small, compact form factor. ICs revolutionized the electronics industry
and paved the way for the development of modern computing systems, consumer electronics,
telecommunications devices, and countless other technologies. They are the foundation of
almost all electronic devices, from smartphones and laptops to automotive systems and
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medical devices. The concept of integrated circuits was first introduced in the late 1950s by Jack
Kilby of Texas Instruments and Robert Noyce of Fairchild Semiconductor. They independently
conceived the idea of integrating multiple electronic components onto a single chip, leading to
the creation of the first practical ICs in the early 1960s. This breakthrough laid the groundwork
for the semiconductor revolution and propelled the electronics industry into a new era of
miniaturization and technological advancement (Kilby, 1976). An example of ICs includes
memory chips, processing chips (CPUs, GPUs), transistors, and resistors, among many others.
Typically, these components collaborate to create intricate computing systems. Alternatively,
they may possess fundamental features, providing more elementary computational options.
Their design process is a complex engineering procedure that covers the combination of
different parts, inclusive of semiconductors, in sealed tight containers and terminals to
interface with the stored instructions. During the design process, it is crucial to verify that the
circuits will perform their intended functions. Even minor errors in manufacturing could render
them unusable. (Dinu & Ogrutan, 2019).
Artificial Intelligence is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the similar task of using computers to
understand human intelligence, but AI does not have to confine itself to methods that are
biologically observable (John McCarthy, Stanford University, 2007). It is also defined as a field
that involves the training of computers to perform tasks with little to no aid from humans
(Kumar et al., 2016). Historical data can be utilized by machines to learn patterns that define
certain processes. With repeated learning, machines can enhance their understanding of these
processes and improve upon them. The ultimate aim of AI is to imitate the problem-solving
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abilities of humans. Although machines lack the unique ability of the human brain to develop
skills and problem-solving techniques, the AI field is combining the learning abilities of
machines with their efficiency to create computers that can learn and improve in increasingly
complex ways. AI has been applied to many fields where automation is possible, making it a
versatile and valuable tool. In recent years, AI has made significant advancements that have
transformed the way we innovate. One of the most notable breakthroughs is the creation of
large-scale language models (LLM), a type of NLP (Natural Language Processing) technology, like
GPT-3, 4, that can understand and generate human-like text. This has led to the development of
chatbots, language translation, and content generation applications (Brown et al., 2020).
Additionally, deep learning techniques have improved computer vision, resulting in advanced
image and video analysis, leading to significant advancements in facial recognition, object
detection, and autonomous vehicles (Dosovitskiy et al., 2020). Reinforcement learning has also
seen impressive progress, which has propelled robotics and autonomous systems to new levels
of sophistication and adaptability (Haarnoja et al., 2018). Furthermore, AI has revolutionized
medical diagnostics and treatment, empowering healthcare professionals with powerful tools
for data analysis and decision-making. AI's integration has positively impacted various
industries, including finance, marketing, education, and healthcare, driving efficiency, accuracy,
and innovation. However, ethical concerns surrounding AI must be addressed to ensure
responsible use, transparency, and the protection of individual privacy and societal well-being.
Reports of the early stages of IC chips designed by artificially intelligent aided by deep
neural network learning have appeared recently (Dinu & Ogrutan, 2019). Over the last decade,
there has been significant growth in the integration of AI technologies within Electronic Design
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Automation (EDA) tools. AI has played a pivotal role in enhancing various aspects of EDA, such
as design automation, optimization, verification, and physical design (Todorov & Dabral, 2020).
Typical AI techniques used in the EDA field include machine learning (ML), deep learning,
genetic algorithms, natural language processing (NLP), and reinforcement learning. Let's delve
into these techniques further.
ML techniques like supervised learning, unsupervised learning, and reinforcement
learning have been used in EDA for tasks such as design optimization, pattern recognition, and
defect detection (Jiang & Yu, 2021). Deep learning, a subset of ML, has been applied in EDA for
image recognition, layout optimization, and design rule checking (Guo et al., 2020). Genetic
algorithms are used for optimization problems, such as floorplanning, placement, and routing
(Al-Jawaheri & Sharif, 2019).
NLP techniques are used in EDA for code generation, documentation generation, and
requirement analysis (Jin et al., 2021). Reinforcement learning has been employed in EDA for
automated circuit design and optimization (Azizimazreah & Esmaeilzadeh, 2021). These AI
techniques have the potential to improve the EDA domain by enabling faster, more efficient,
and intelligent design processes.
In the world of artificial intelligence, Large Language Models (LLMs) are considered a
significant advancement and are currently being utilized in the EDA field for various tasks.
These tasks include automating the verification of electronic designs by generating test cases,
which can expedite the verification process. LLMs are also useful in generating documentation
for electronic designs, improving their readability and understandability. Furthermore, LLMs
5
can suggest improvements to electronic designs, enhancing their performance, reliability, and
security.
Although Large Language Models (LLMs) have impressive capabilities in natural
language processing tasks, they do have certain limitations that are important to consider for
the IC design domain. One of the major limitations is the high computational cost for training
and deploying large LLMs, which can be expensive and time-consuming. Additionally, LLMs may
struggle with out-of-domain or rare inputs, despite their strong performance on common tasks.
This limitation may lead to numerical errors which is crucial when analyzing electronic circuits.
Furthermore, LLMs heavily rely on the data they are trained on, which can make them less
effective when dealing with inputs that are significantly different from the training data.
(Hutchins et al., 2021). These limitations, particularly in emerging areas like deep learning and
large language models, pose a challenge in implementing rapid advancements in the IC design
field. The rapidly evolving nature of technology suggests that there will be significant progress
in the fields of AI and IC Design in the near future. This will involve new breakthroughs and
more intricate processes
Study Problem
The problem lies with microchips design challenges due to high costs, design complexity,
and difficulties in verifying IC circuits, especially in advanced manufacturing nodes of 7nm and
below. Microchip designers face significant challenges that modern technology has yet to
overcome. To address this issue, AI technologies are being explored as a potential solution.
(Kovacs et al., 2020). The relentless progress in semiconductor design and manufacturing has
led to a rapid and exponential growth in the complexity, functionality, and performance of
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integrated circuits. Traditional design, verification and manufacturing methodologies struggle
to keep pace with the growing demands and to provide efficient programmatic solutions to
complete microchip’s project in a reasonable timelines (Sangiovanni-Vincentelli et al., 1994). To
address these challenges, the integration of Artificial Intelligence (AI) techniques has emerged
as a transformative solution.
The verification process is one of the most challenging aspects of IC design, as it requires
considerable time and resources (Dinu & Ogrutan, 2019). There is a growing demand for
machines with deep learning AI capabilities to aid in IC design and verification from the outset.
Once engineers have designed the circuits and their intended functions, it becomes difficult for
machines to confirm their performance post-manufacturing. The list of items that need to be
checked is extensive, including working power ranges, transistor properties, and temperature
ranges. (Shoniker et al., 2017). Engineers can assess the performance of ICs and deduce
whether they work or not (Miranda, 2020). However, the widespread applications of these
circuits mean that each performs unique functions. An adaptable verification process is hard to
perform, especially when automation is needed. The changing environmental conditions
further complicate the operating efficiency of these circuits. With the combination of
technologies still limited, the study aims to reveal the shortcomings and why it has yet to be
widely adopted.
The reasoning behind this is that the performance and quality of vital computer
components can be enhanced with the appropriate use of AI methods. The self-learning nature
of deep learning AI points towards a more advanced manufacturing process beyond current
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levels. The revelations will help the practitioners in the IC design field by ensuring a more
accurate verification process with possibly fewer errors and faster.
Despite the use of AI techniques for over a decade, there is still a pressing need to
improve the design of IC for custom tasks. It appears that the adoption of AI technologies in
EDA design tools is progressing at a slower pace than expected. The growing complexity of chip
design highlights the need for new and improved automation processes to achieve optimal
results. However, current technology still requires enhancements to enhance automation.
Additionally, there is a gap in finding ways to reduce the costs associated with chip design, as
current methods are expensive. To address this issue, research should focus on identifying
more cost-effective alternatives. Furthermore, there is a need to improve the integration of
technologies such as machine learning and artificial intelligence, which represents a third gap.
Therefore, it is crucial to conduct research to develop modern solutions that can close this gap.
Study Purpose
The purpose of this qualitative exploratory study was to investigate the gap between
the use of AI in technology and its integration into IC design flow. A recurring theme in
numerous previous cases is using qualitative-based research approaches. These approaches are
backed up by the need for the area of study to provide meaningful contributions to computing
in terms of performance and better designs (Obermayer & Tóth, 2021). The nature of
technology is mainly focused on improving devices for managing the ever-increasing demands
of processing. This condition has prompted computer research to focus on their inner workings
translating to support by qualitative methodologies. Further, an exploratory design was
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deemed appropriate for this study because it matched up the need to investigate existing
approaches at the time to solutions and look for possible improved ones.
The population of the study was the experts connected to the design of computer chips
through modern approaches. This population covered the practitioners in the computing fields
who partook in creating better-performing ICs. The choice participants were individuals who
dealt directly or indirectly with the designing of ICs, practitioners in the AI and ML fields, and
possible researchers who were concerned with the progress of AI within the IC design arena.
The research had a sample size of eight participants, chosen through purposive and
convenience sampling methods. This is a non-probabilistic sampling approach that utilizes
participants who best fit the study (Indrayadi, 2020). The reason for choosing this approach was
that the area of study is quite narrow and requires a high set of specialized skills. This means
that the participants had knowledge of the fields in which the topic sought to investigate.
Recruiting then occurred through the utilization of a professional network. Participants needed
initial negotiations outlining the purpose of the research and the objectives aimed to be
achieved. As such, voluntary consent was utilized before continuing any questions or activities
posed to these participants. The data was collected through a questionnaire distributed to the
participants online. This questionnaire covered both closed and open-ended questions to
gather a wide range of knowledge from the participants. The interpretation of the data
collected from the participants occurred through the Microsoft Excel tool to identify patterns
and themes. The findings were used to compare with the results of secondary data from
journals regarding the situation.
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The need to create more complex circuits is ever-rising, with the experts having a hard
time keeping up with these needs (Marinova & Bitri, 2021). Gathering as much information as
possible while keeping to the topic and conducting a study to interpret different viewpoints of
IC design helps clear up the complexity. With the extracted knowledge, the study findings
produce a set of recommendations and possibilities that could improve the verification process
in ICs design. Further, the knowledge from the study expands on the existing understanding of
AI and IC design. As the use of AI in IC design is still improving, the study expands the possible
solutions to an existing problem. Further, careful research revealed whether existing studies in
the areas showed any signs of bias or insufficiency. The identified literature gaps in the study
form a basis for possible future study cases while identifying what needs to be done for an AI-
led area.
Research Question
The research question aims to investigate how AI can work together with IC design to
tackle the challenging problems faced by modern IC design processes. To address the
disconnect between these two areas, bridging the gap is crucial. This obstacle poses a
significant challenge for IC design, and the research aims to explore viable solutions. The
objective is to significantly enhance the design flow of integrated circuits, leading to improved
performance and power efficiency in computers and electronics.
Q
How can AI be implemented within IC design, and if so, what are the options?
Conceptual Framework
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Figure 1 represents the study’s conceptual framework. The key focus of the study
centers on AI and ICs. These are the top-level areas the study seeks to improve by finding
existing problems and suggesting solutions. AI divides into subsections where each segment
represents a contribution to the study area. Since AI has received much focus and is in the
process of widespread implementation, it still faces problems associated with its usage
(Khakurel et al., 2018). These problems form a key part of the study as they help outline the
limitations the study faces. The history of AI is also crucial in the study as it builds the picture of
the issues associated with AI. An additional part on the implementation of AI in the computing
industry helps reveal the possibilities of its usage in ICs. AI is also closely related to ML, and the
inclusion of the latter is critical to this study.
The IC sector covers the processes involved in designing and creating microchips. The IC
topic boils down to the design area, which is the main focus of this study. The IC design process
is the main area that has so far introduced limitations in advances in computing (Stopjakova et
al., 2018). This area is further subdivided into history and challenges faced throughout the
design process. Understanding the process is essential for pointing out the possible
improvements and fixes relevant to the study.
Throughout the years, various AI/ML technologies have been incorporated into IC
design tools to enhance the design process, improve efficiency, and optimize different aspects
of the design flow (Q. Liu et al., 2019). Some of the prominent AI/ML technologies used in IC
design tools include machine learning algorithms that optimize design parameters and
performance metrics such as power consumption, speed, area, and reliability, resulting in
better design outcomes (Mohanty et al., 2014). Predictive analytics are also utilized to predict
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potential design issues or bugs early in the design cycle, helping designers identify and solve
problems before they become critical. Design Rule Checking (DRC) ML algorithms are used for
efficient and accurate DRC, ensuring designs comply with the required manufacturing rules and
reducing the risk of fabrication issues. ML-Based automated layout generation automatically
generates IC layout designs based on design constraints and specifications, speeding up the
design process and reducing human effort. AI/ML methodologies are utilized for high-level
synthesis and optimization of register-transfer level (RTL) designs, resulting in better
performance and reduced power consumption.
Physical Design Automation AI/ML techniques are used in place and route algorithms to
optimize chip layouts, improve signal integrity, and reduce design cycle time. One of the most
important topics is timing analysis and closure ML algorithms to assist in timing analysis and
closure by predicting critical paths and suggesting design changes to meet timing constraints.
Another important aspect is silicon yield prediction AI/ML models to predict chip yield and
identify potential manufacturing defects, enabling designers to make informed decisions during
the design phase. In the mixed signal domain, Analog and RF Circuit Design AI/ML technologies
have been integrated into analog and RF circuit design tools to optimize transistor sizing,
improve matching, and enhance overall performance.
The use of AI/ML technologies in photonic IC design tools is also becoming more
prevalent, providing numerous benefits throughout the design process. Inverse Design and
Optimization AI/ML algorithms are among the technologies that have been integrated into
these tools. They are capable of performing inverse design tasks, where specific optical
properties are identified for a photonic IC, and the AI model generates the corresponding
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structure or layout. This allows for efficient optimization of complex photonic devices with the
desired functionalities. Device Simulation and Modeling AI/ML algorithms are also used to
simulate and model the behavior of photonic devices, such as waveguides, couplers, and
resonators. ML models can predict device performance, including transmission characteristics
and resonant frequencies, without the need for full numerical simulations, which saves
computational time. Material and Process Optimization ML techniques are used to optimize
material properties and fabrication processes for photonic ICs. Photonic Circuit Synthesis AI/ML
algorithms synthesize photonic circuits by automatically selecting and connecting photonic
components to achieve specific functionalities. This speeds up the design process and enables
the exploration of complex photonic circuits. Additionally, AI/ML technologies can be used for
process control and monitoring during fabrication to ensure high-quality and consistent
photonic ICs (V. Verma et al., 2020). It's important to note that the AI/ML field in photonic IC
design is rapidly progressing, with ongoing research being conducted to incorporate AI
techniques into different stages of the design process. With technology advancements, AI/ML is
anticipated to have a more substantial impact in facilitating quicker, more efficient, and
inventive photonic IC design.
The final section involves the combination of the knowledge gathered in the IC design,
AI, and ML. The areas combined include the general knowledge of AI and ML, the changes
experienced in AI, and the changes present in IC design. This knowledge is the main highlight of
what it takes for customized chips with AI capabilities to be created. The knowledge converges
to the possibilities AI and ICs can have and makes up the final core contribution of the study.
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The study obtains inferences from these topics, collects the problems, and seeks relevant
solutions.
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Figure 1
Conceptual Framework.
Significance of the Study
The literature review in this study provided a key overview of the fields of AI and IC
design. A majority of the identified literature focuses on innovating methods where one area
helps the other (Chen et al., 2022) and (Moness et al., 2022). However, the study provided a
necessary look into the problems identified so far in general and the solutions so far or the gaps
existing. The study is key in contributing to research as the identified issues are set to be
handled and integrated into computing design for better results. The study also contributed to
the technological field and academic area by helping reveal how various methods can be
effective in the field of building better computing chips and the revelation of more solutions
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areas for research. It is significant that modern research keeps up with technology in providing
meaningful conclusions.
Researcher Positionality and Reflexivity
This research topic is an interesting area in my related field, which covers the
technology behind computers. The motivation to get involved in circuit design and the inner
workings of computers has so far grown from the early tinkering with devices of all sorts.
Choosing to focus on AI and ICs design is also set to expand my understanding of machines
while meaningfully contributing to the field. The research is interesting and shows that the
combination of the two areas will soon lead to interesting innovations. The position as an
enthusiast and practitioner in the technology field is set to contribute to the knowledge of AI
utilization at the core of chip design and manufacturing. This understanding is set to fuel the
need to finish up the research and contribute to technology in general with no intended bias.
Use of reflection and sound study methods will be employed to reduce the potential for bias.
Delimitations and Limitations
Delimitations represent the extent of the aim and questions posed by the research as
highlighted by the researcher (Theofanidis & Fountouki, 2018). One of the key delimitations of
this study is the exclusion of covering the cutting-edge discoveries and technologies in the field
of circuit design in the literature review. The reason for the exclusion was brought by the need
to focus on an approach that identifies the problems caused by manual processes. The
unverified and cutting-edge technologies are limited in substance as such changes take time to
implement and integrate into the existing designs. The research was also set to only focus on
the fields of AI and ICs design. Any topics that deviate from the two were only briefly touched
16
upon if they fit within the research context. The necessary skillset needed for the participants
was in computer science with specializations in circuits design or AI. The study expanded
geographical limitations and included online-based surveys for participants from any part of the
world.
Limitations in the study are the points that are weak and beyond the control of the
researcher (Wells-Cornwall, 2021). While theoretical understanding is necessary before
practical applications, this approach is set to carry over weaknesses experienced in the existing
studies (Shi, 2018) and (Stopjakova et al., 2018). The limitations include circuits being prone to
sensitivity to temperature, small available spaces, and noise in the circuits. Other studies
pointed to the need for more focus on the practical applications of artificial intelligence with a
thorough exploration of existing solutions. The result of such an approach was a limited
understanding of the exact contributions these studies provide to the solutions. The need for
an adequate look at history while being innovative, therefore, was large. This study was limited
to obtaining data from only a small sample size, although this is appropriate for the qualitative
nature of the exploration needed.
Definition of Terms
The terms that follow cover the major focus areas of the study and are necessary to get
the complete picture of how they relate to the research. The terms focus on the technical
aspects of IC design and computing.
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AI
Artificial Intelligence (AI) is defined as applying advanced analysis and logic-based
techniques, including machine learning (ML), to interpret events, support and automate
decisions, and take actions. The definition provided aligns with the present and developing
state of AI technologies and capabilities. It recognizes that AI often involves probabilistic
analysis, which combines probability and logic to assign a value to uncertainty.
Circuit Verification
Circuit verification is a process of identifying how circuits correctly perform their
designated functions and whether they produce any errors (Manolache et al., 2022).
Integrated Circuits
Integrated circuits are electronic components made of semiconductors containing tiny
transistors, diodes, and capacitors used to process information (O’Regan, 2018).
Machine Learning
ML is a subset of AI field, focusing on the approaches and processes that allow machines
to acquire knowledge and use data to improve their tasks (Alexopoulos et al., 2019).
Robotics
Robotics is a branch of engineering that covers the creation and use of programmable
machines (Moradi et al., 2019).
Chapter Summary
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This introduction chapter has outlined the initial information of the dissertation and
what areas it covers. To recap, the dissertation focuses on the implementation of AI in the
design of ICs. These areas are key to more efficient machines and ones that have been designed
with more complexity. The study opportunity was to reveal the shortcomings so far and how
they have been handled so far. This revelation identified the gaps and issues that need
addressing in the future implementation of IC design. The research question focused on
revealing the possibility that AI could be utilized to enhance the design process of ICs. The study
was significant in contributing to technology, revealing more topic areas for future studies, and
expanding the knowledge of computing. This study only focused on the above-mentioned topic
areas and involved a theoretical approach to accomplish its goals. A deeper dive into AI, ICs,
and their design was outlined in the coming literature review chapter.
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Chapter 2: Review of the Literature
The problem lies with the high costs of designing chips, difficulties in the verification of
IC circuits due to the complexity of the process, and limitations in the automation of the
verification process, which modern technology needs improving on and intends be handled by
AI (Kovacs et al., 2020). The purpose of the qualitative exploratory research was to explore the
disconnection between AI applications and IC design. The conceptual framework of the study
centers on the implementation of AI in ICs design, which is a lucrative area that is changing
modern computing (Yantır et al., 2022).
This literature review chapter is an important section of the dissertation as it reveals the
existing material and knowledge relating to the areas of study. The material utilized in this
section is peer-reviewed papers, conference papers, and any additional material that is relevant
and verifiable. The chapter is organized around AI and ICs with additional topics which are
derived from the areas, as depicted in the conceptual framework for this study (Figure 1).
Further literature is provided on the devices and applications in AI and the design process of
chips, and how it has changed over time. Additional material is presented on AI and IC design,
ML and IC design, applications of AI in IC design, and the progress so far on AI and ICs design.
The chapter also covers the automation of processes in ICs design, the verification of ICs,
hardware verification processes in combination with AI and more. The last sections of the
literature material cover the trends occurring in IC designs and a small coverage of detecting
issues in ICs. An additional section is present on the gaps identified in the literature and why
they are important to this dissertation. The chapter ends with a conclusion and summary.
20
Literature Search Strategies
The literature search was conducted through a combination of the ProQuest
Dissertation and Theses database combined with a search on the popular research search
engine Google Scholar. The chosen articles are related to AI and IC design. The paper choice
comes through a brief overview of each article and seeing the knowledge it contributes.
The keywords used in the search for the documents include artificial intelligence,
integrated circuits, integrated circuits design, implementation of AI in IC design, the progress of
AI, machine learning, machine learning models in IC design, errors in IC design, limitations of AI
in IC design, and automatic IC design. These terms are created through frequent updating of the
search process and tuning what brings up the most relevant results. The next sections explore
various materials covering the target topics.
Artificial Intelligence (AI)
AI is a critical part of the modern world with its contribution to computing (Berente et
al., 2021). Technology has so far altered our approach to problems, as humans are starting to
solve problems previously considered unsolvable. The contribution of AI has also so far changed
the pace of computing to bring about so many changes than the previous computing
contributions combined (Li et al., 2019). The current research is geared in a direction seeking to
improve lives and achieve a deeper understanding of various topic areas. The possibilities are
endless in terms of the implementation of AI, but the major hurdle is towards overcoming
current challenges (Berente et al., 2021). These technological challenges are also part of the
issues to face with other areas covering security, ethical considerations, data handling, rights,
social implications and more. The issues being brought up are a necessary step and show the
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progress so far achieved in integrating AI (Berente et al., 2021). Such a massive advance in steps
means that AI is single-handedly changing so many areas of life currently and is expected to
contribute more. AI is indeed at the frontier of modern achievements.
These advanced changes in computing can be attributed to the ever-improving changes
in the processing capacity of chips (Lu et al., 2018). Performance in computing chips has been
steadily increasing each year, leading to better capabilities of each generation of computers.
Coupled with the improvements, the inclusion of big data in processing is also adding to the
capabilities of AI. The changes, therefore, are synonymous, and the large capabilities can be
attributed to these iterative additions. Liu and Chakrabarty (2021) also point out the eventual
need for AI to operate at similar capacities and capabilities to the human brain, an ambitious
move set to reveal key details in computing and in solving major problems that have been
available throughout human existence.
Datasets powering technology and AI keep getting larger as access keeps on getting
easier (Wu et al., 2021). These changes mean that AI is expanding at a fast rate in all industries
and areas. Significant challenges, as illustrated by Wu et al. (2021), show that the
implementation is posing limitations with the current capabilities humans possess. The growth
of data is occurring at a larger pace than the progress in keeping up with its processing,
whether in storage, processing, or its utilization in decision making. Hampton (2020) points out
the need to be innovative and quickly come up with the necessary tools to balance out the
growth rates.
Different-sized machines are also claiming a stake in their contribution to AI (Srinivasulu
& Ravariu, 2020). From large equipment capable of spanning buildings to tiny devices that
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might be invisible to the naked eye, AI is changing all areas. Nanotechnological devices and
tools are also seeking to adopt AI and enhance the future of computing and gadgets. These
devices, as reported by Alrubei et al. (2022), might be more efficient in terms of costs and
integration in different areas. The downsizing of the tools also means that their spreading to
more capabilities will not be limited by size, portability, or restrictions of current machines.
The sentiment on AI operating at a scale to the brain has prompted issues on its
replacement of human workers (Hassani et al., 2020). These worries come from the ever-
improving robots powered by autonomous machines, which work at better rates and efficiency
and without the need to take breaks. Davenport and Ronanki (2018) showcase the possibility
that such a case creates panic among everyone. However, their study shows that most jobs are
not about to be easily automated and push people out of their positions. This means that AI is
not yet efficient enough to replace human workers.
A review of the current literature and direction of AI and robotics by (Li et al., 2019)
shows a direction toward mimicking naturally occurring items like humans and animals. These
machines are built to learn about the movements of the items and learn from the items that
are not working. The eventual progress leads to a set of machines that can keep on improving
to perfection. The AI models used to train and power these robots are improving each passing
year (Alam, 2021). The number of laboratories, institutions, and private companies rushing to
achieve such autonomy is quite high. These moves are set to improve the performance capable
of modern robotics and computing machines. The approaches in training the machines are split
into top-down solutions where the larger problem is subdivided into smaller sections and a
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bottom-up approach concentrating on the combination of smaller solutions to solve more
complex ones.
Generative Pretrained Transformers (GPT) is an advanced AI technology that has
revolutionized the field of natural language processing. These models are built on the
Transformer architecture and are pre-trained on vast datasets to learn language patterns and
structures. GPT models excel in generating human-like text based on given input or context.
One of the most notable GPT models is GPT-3, developed by OpenAI. It is one of the
largest language models to date, with 175 billion parameters. GPT-3 has proven to be highly
effective in tasks such as text generation, translation, question-answering, and language
understanding.
GPT-4 was released on March 14, 2023, and is currently available in limited form
through the premium chatbot product, ChatGPT Plus. Access to the GPT-4 based version of
OpenAI's API is provided through a waitlist. The number of parameters in GPT-4 is unconfirmed,
with some speculating that OpenAI has used around 100 trillion parameters, while others claim
it has 1.76 trillion parameters.
The pre-training process of GPT involves training the model on vast amounts of text data
from the internet, enabling it to learn grammar, syntax, and semantics. This pre-training is
followed by fine-tuning on specific downstream tasks to adapt the model for more specific
applications.
GPT and other generative models have various applications in natural language
processing, creative writing, chatbots, language translation, content generation, and even code
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generation. These models are pushing the boundaries of what AI can achieve in language
understanding and generation tasks.
AI has an ultimate goal to achieve superintelligence (Müller & Bostrom, 2016). The
current advances are still in their early form, yet to get to general intelligence. The next massive
step in AI is expected to reach superintelligence. This state is possible where the machines will
have capabilities in processing information beyond the point and comprehension of humans
(Lee, 2022). The intelligence is also expected to span several areas, and the machines will sail
through without any issues.
It is expected that AI is soon set to infiltrate every part of human lives and interactions
(Agrawal et al., 2017). From the internet, social media, and more, AI is already powering most
of the behind-the-scenes choice of content for everyone. This slow creep into everyday tasks
means that AI will go deeper than personal lives and alter our interaction with it (Sparrow &
Hatherley, 2020). It is set to be part of human lives and involved in making decisions, interacting
on our behalf, and more. The capabilities of AI have since been elaborated in multiple types of
research and show an infiltration beyond current technology.
AI Devices
The technology utilizing AI is widespread across different industries such as robotics,
transportation, health, and sensing (Srinivasulu & Ravariu, 2020). This observation shows that
devices are getting better at processing information and will soon be autonomous. Nano
devices are also included in the utilization of AI, but their progress is slow following limitations
in circuit design (Srinivasulu & Ravariu, 2020). The teaching region is also experiencing the
utilization of AI (Bailey, 2019). This area is crucial in providing learners with the correct basic
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skills to experience an ever-improving cycle associated with AI. AI is also set to personalize the
learning experience through such adaptation. The major effort responsibilities, therefore, lie
with the semiconductor manufacturers who need to create devices that match the software
demand of AI (Batra et al., 2019). The extent of devices expected to handle AI and integrate it
into everyday lives, therefore, keeps growing as the capabilities and manufacturing capabilities
expand (Batra et al., 2019).
Integrated Circuits
Integrated circuits have altered the entire electronics industry by introducing their
unique approach to devices (O’Regan, 2018). Their evolution from large sizes containing a few
transistors to tiny items with millions of transistors shows major progress in the manufacturing
and computing world. The cost of constructing these circuits also reduced dramatically, leading
to their inclusion in almost all electronic devices currently (O’Regan, 2018). Hao et al. (2021)
highlighted that the improving circuits are ever getting smaller and more powerful following
Moore's law. Moore’s law states that the transistor density in chips doubles every 2 years (Xiu,
2019). Modern ICs make up most of the processing technology and have changed through
different generations to be extremely complex devices.
The processing of the chips takes different approaches, which involves the chemical
processing of the semi-conducting material to allow the processing needed (Ilagan et al., 2020).
Algorithmic processes have been invented to create complex circuits at a tiny scale, with some
having a few nanometers separation. This scale indicates the tiny margins of error that the
manufacturers work with and the many possibilities of errors occurring (Ilagan et al., 2020).
Some technologies, such as the nanoimprint lithography technology, are variations towards
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getting the most precision in the devices (Sreenivasan, 2017). The approaches seek to go
further down and handle the semiconductors at a molecular level, which is a long stretch
considering the initially ICs developed after their discovery.
Chip Design
The increase in complexity of chip design has been on a steady rise with the
development seeking shorter design and development times (Obilisetty, 2018). Chip design
stands as one of the most complicated processes in creating computers or electronic items
(Richelli, 2021). This complexity arises from the large number of variables needed to be
considered when handling the chips to make them reliable, resilient, and more. The need for
chips that use low voltage is also rising, further complicating the design process (Richelli, 2021).
Some relief is pointed out by Obilisetty (2018) on data and intelligence-based approaches being
applicable to ease the processes of designing and creating chips. Other approaches are seeking
to create designs that are aware of the energy they use and can route the processing
depending on conditions (Chaudhary et al., 2020). The approaches are also raking up the
performance and speeds of the chips while trying to design them for operating in multi-chip
environments.
An overview of chip designs from their invention in the 1960s shows a change from tens
of micrometers to recent micro distances of a few nanometers between the circuits (Liang,
2021). The designs and manufacturing are a tight race for the most efficient and powerful
designs that are quickly implemented into everyday items. The inclusion of photonic ICs also
expands the capabilities of modern chips in terms of applicability (Kish et al., 2018). This further
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expands the possibilities of chip designs to exclusive functions such as optical-based technology
and where the processing is more efficient in their applications.
The current general direction is towards the best performance, high security, very low
power consumption, and well-connected ICs (Kulkarni, 2020). Modern designs are also evolving
to customize chip design and manufacturing for each industry for a particular functionality.
Kulkarni (2020) is keen to highlight these changes and state the declining application of analog
designs in favor of automated ones.
Artificial Intelligence and Integrated Circuits Design
AI is creeping into many different areas, including IC design, where special chips are
being invented for its execution (Monroe, 2018). The computational needs and power
consumption of AI mean that the traditional ICs are not well equipped to handle it. The result is
the creation of hardware that targets ML and AI needs (Monroe, 2018). The design process of
ICs for the implementation in AI means that the chips need to be verifiable in terms of
mathematical specifications (Seshia et al., 2022). Seshia et al. (2022) also outline that the
specifications of AI chips are at a different level from typical ones. AI-based chips need to
operate in a distributed manner far from the central-based designs of typical computers
(Shastri et al., 2021). These diverse chips are set to mimic the diversity of brain cells which
operate in unison despite being stand-alone in their actions.
There exist some approaches that focus on applying AI and ML to Integrated circuits
design. Approaches such as the Very-large-scale integration (VLSI) are used in designing and
testing circuits and use AI to perform automation and repetitive tasks (Amuru et al., 2023). The
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scale of IC design has also increased to the point where human engineers cannot design circuits
without the help of machines.
Some special types of IC designs need to integrate functionalities such as wireless,
sensors, and more (Fayazi et al., 2021). The need for these complex chips for multi-purpose use
means that their design, verification, and consumption of power need to match the changing
times. The chips also need to be secure as the cases of cyber-attacks, and the need for data
protection continue to rise (Xu & Zhang, 2020). Xu and Zhang (2020), in this case, are focusing
the design process on handling the fast-growing insecurity cases on computer-based machines.
The propositions are towards designing the chips from the ground up with tight security and
preparing for a tough environment (Xu & Zhang, 2020).
Machine Learning and Integrated Circuits Design
A revisit of Moore's law points to an exponential increase in chip power and
performance with every passing year (Khailany & Dally, 2020). Modern approaches are being
utilized to accelerate the performance of chip design that covers different areas such as their
verification, testing, timing, and more (Khailany & Dally, 2020). ML approaches are currently
being utilized in this modern design to accommodate the complexity and data requirements (Li
& Franzon, 2016). All manufacturers are currently rushing to use the latest approaches in ML to
increase the manufacturing process and ensure its eventual success. ML has since proven to be
excellent in improving these processes (Li & Franzon, 2016). The pressure in competition on the
ICs design pushed early designers to seek alternative approaches to the processes (Kahng,
2018). These processes have since been replaced by predictive models that look for any issues
in the pipeline and estimate how well they can be solved (Kahng, 2018).
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There is an ever-increasing need to design chips that are cheaper in terms of
development, integration, maintenance and long-term use (Saito et al., 2020). The researchers
point out that such an achievement requires complex expertise in creating the best chips and
ensuring that they can last the intended lifetimes (Saito et al., 2020). The testing process is
highlighted as the most important part of developing IC chips (Amrouch et al., 2021). These
issues on current ICs point to utilizing ML for a deeper analysis that the analog-based processes
cannot comprehend (Amrouch et al., 2021).
The automation of the design of analog circuits through ML and artificial neural
networks (ANN) is also an ideal approach (Devi et al., 2021). The ANN models provide a self-
improving approach different from the typical ML algorithms and with better expectations in
the outcome. However, all the design processes boil down to the reliability of every single chip
that the ML approaches are trying to handle (Jiahe, 2020). The general agreement seems to
center on applying advanced ML models to seek out problems and suggest the best approaches
to make them affordable for the developers and their applications (Jiahe, 2020). Open sourcing
the design processes or the optimization options is also proving to be a desirable approach
(Zezin, 2022). The growing complexity of Integrated Circuits design means that the variables to
consider are ever-increasing, leading to the need to include as much expertise as possible
(Zezin, 2022).
The applications of ML in IC testing are also being steadily expanded to overcome the
existing problems (Stratigopoulos, 2018). With the availability of large datasets being integrated
into ML processes, detecting any issue in design and manufacturing is proving much easier
(Stratigopoulos, 2018). These changes have been forced by the rapid changes in technological
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advancements that have since been hard to keep up with (Afacan et al., 2021). The changes can
be attributed to the need for accurate responses and the challenge of keeping up with fast-
paced competitors. Automation tools for analog circuits and chips are increasing at a high rate
and proof of the demand (Afacan et al., 2021). The traditional approaches have since been
declared inefficient and time-consuming, which do not add up to the expected results (Mina et
al., 2022). Manufacturers are currently relying on the input from ML algorithms to improve
efficiency and point out the areas needing improvement or replacement (Mina et al., 2022).
A different approach focuses on the design and development of chips designated for ML
(Sze et al., 2017). This approach aims to identify the changes in trends and create the exact
chips that can easily be applied for ML, robotics, and the Internet of Things (Sze et al., 2017).
The identified opportunities for ML in IC design revolve on pushing the limits of the problems
presented by electronic designs (Wang & Luo, 2019). Such problems are frequent in a world
geared towards the eventual overcoming of chip design challenges and integration and use of
multi-chip machines (Wang & Luo, 2019). ML is indeed a recurring concept in designing modern
chips and ensuring their affordable nature and high performance.
AI Applications in IC
It is impossible to separate AI and ICs (He, 2021). This combination comes from the
dependence of AI to run on ICs at the core. The performance of intelligence and learning ability
comes from how efficiently the ICs have been designed. The growing complexity has seen the
application of AI in business processes (Pheng & David, 2022). The IC manufacturing industry
has also seen the use of AI in recent times, where the demand for the best performances keeps
increasing. The timely need to deliver the chips at the correct time is also ever-pushing.
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Circuit logic simulation is a critical area where AI is set to revolutionize (Lin, 2021). The
application also extends to the optimization of 3-D ICs and the systems they power (Park et al.,
2017). These areas are key in determining the future of AI, as they mean the best outcome for
computer chips. In the logic simulation, AI applies to checking the different combinations
possible and the best way to improve them (Lin, 2021). These improvements lead to the
scrutiny and handling of any errors that might arise in the processing of information. The 3-D
integration processes contribute to more dense systems, which eventually bumps up the
performance.
Another approach is in distributed AI, where special circuits are being built to process
data on wireless networks of sensors (Chêne et al., 2022). This area is expected to improve on
the current wireless networks and more adaptive processing using AI. The possible challenges
to solve are security, energy efficiency and more.
Progress on AI and IC Design
The progress AI has contributed to IC Design is massive so far. AI is bringing about the
optimal design of modern chips by easing the process (Beres et al., 2015). The example of AI
being used for passive damping to handle the issues brought about by the resonance coming
from high-order filters shows progress. Further, AI is also used in the testing processes where
the traditional methods are costly and time-consuming (Liu & Chakrabarty, 2021). The
application of AI and ML in these tests ensures that the optimal solution is picked and thus
reduces the entire cost of testing these chips (Liu & Chakrabarty, 2021).
Processing in memory is also a new type of approach which are turning out to need a
new iteration of IC chips (Kim et al., 2022). The AI and ML approaches are being utilized to
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handle the high processing functions and processes needing large amounts of data. The trends
point toward better-designed chips at the bank level that are much more energy efficient than
the traditional ones (Shanbhag & Roy, 2022). The mapping of the in-memory computing
processes in the design processes is proving a complex undertaking that ML is helping on. The
advantages are also evident in the scaling process of the technology in terms of energy
efficiency and handling the high-density circuits (Shanbhag & Roy, 2022).
Miceli et al. (2022) mentions the application of AI to design chips utilized in deep space
astronomy. Such actions mean the transfer of data and processing is high and needs high-
performance IC chips. The features being introduced by AI are shown to be better at scaling the
computing processes on specially made chips (Miceli et al., 2022). An additional area lies in
photonics, where neural networks are utilized to design the necessary chips (Alagappan et al.,
2022). It's important to note that the field of AI/ML in photonic IC design is rapidly evolving, and
more research is being conducted to explore and integrate AI techniques in various aspects of
the design process. As technology advances, AI/ML is expected to play a more significant role in
enabling faster, more efficient, and innovative photonic IC design.
Automating IC Design
The process of automating the design process of ICs is a developing concept aiming for
the accomplishment of more complex approaches (Huang et al., 2021). This automation is
growing fast following the training of ML models, which are accumulating knowledge for the
best outcome. The traditional approaches are set to be overcome by the automation ones
solely from the knowledge accumulation of the design process (Huang et al., 2021).
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Analog and physical design of circuits had dominated their creation for a long time
(Youssef et al., 2020). The suggestion of using frameworks to utilize look-up tables to compute
the circuits is gaining traction. This approach is seen as more accurate and faster when it comes
to simulating the design process (Youssef et al., 2020). The process is also said to enhance the
decision-making process and innovative options for IC design processes. The analog design
process is also a process that relies heavily on the experience to explore the tweaking options
of the simulation process for better circuits (Youssef et al., 2020). The major issue in this
process, termed a bottleneck, is this part costs more in terms of resources and time necessary.
Electronic Design Automation (EDA) tools have since emerged to replace traditional approaches
(Nardi et al., 2019). For autonomous driving applications, the design of extra complex chips is
needed at a high pace (Nardi et al., 2019). The automation potential is steadily increasing as
better implementations and regulations are devised (Nardi et al., 2019).
The complexity of IC designs in the matter of cost is also termed as a large barrier in
designing circuits (Kahng, 2018). It is termed more costly to scale down circuits for more dense
circuits, which improves the performance and allows for better processing. The opportunities
for automation and better designs are wide and are set to alter the entire computing industry
(Kahng, 2018). It is also clear that automation balances the process by better inclusion of ML,
better contribution from designers, and deeper integration of more design tools. Demand in
the ICs for improved computing options is also significantly high (Gubbi et al., 2022). EDA is
currently termed as a necessary enabler of IC designs and the semiconductor industry in
general. The learning approaches currently available have potential improvements by applying
ML methods as their source of analysis. The accumulation of data for the learning process is
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also expanding as designers are exploring better alternatives (Gubbi et al., 2022). These
improvements are experimented across all types of ML learning, such as supervised,
unsupervised, semi-supervised, and reinforcement learning. Casting a wide net on the
possibilities is viewed as a better option in revealing key contributions to better ICs (Gubbi et
al., 2022). From getting rid of unnecessary designs and optimizing the best existing processes to
exploring innovative ways for physical designs, the opportunities for automation are endless.
The feasibility of automation options is definitive, given the situation in the physical
design of circuits. A ML approach by (Islam, 2022) showed a reduction in 98% of violations
associated with IC design. The implications of automation's contribution to computing are huge
and anticipated as the expected next move (Islam, 2022). A suggestion by Gubbi et al. (2022)
combines the greedy searching algorithm with an ensemble approach to get better
performance for detecting issues in circuit design. The result is better efficacy of the algorithms
and which is intended to improve general ICs.
The layout of the Integrated circuits is also an important part to consider in the analog
IC design (Scheible & Lienig, 2015). The process of designing analog ICs also takes time, with
automation coming in handy to reduce it significantly (Porrasmaa, 2021). The newer
approaches are integrating approaches that overcome the current constraints and creating
designs that are context-aware (Scheible & Lienig, 2015). Tests are showing the approaches
might soon be integrated as the complexity of ICs keeps on getting complex. The reusability of
the automation processes is high, which points to the maximum optimization of integrated
designs in the future (Porrasmaa, 2021).
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A new approach called the Analog Layout, Intelligently Generated from Netlists (ALIGN),
is set to automate the layout of analog circuits and shows signs of good performance (Dhar et
al., 2020). This approach utilizes algorithms and templates to ease and make the designs much
better than the traditional approaches. Integrating AI in the learning process is so far improving
the errors made by other automated processes. The demand for better hardware is changing
the design scene by introducing the need to explore alternatives in the design process (Mina et
al., 2022). The encouraging results from this integrated approach show a better future in IC
design (Takai, 2020).
Some developments in the design of ICs are introducing unforeseen challenges in the
automation process (Chu & Ozdal, 2018). The nature of the design process shows that it might
need new ML models to try new approaches.
Circuit Verification
Verifying circuits for errors is an ever-important part of their design and is getting more
complex (Fan et al., 2020). Designers have a tough job of creating the circuits within tight
restrictions but are expected to perform their best. The verification process designers are
slowly adopting systematic approaches to meet these restrictions (Fan et al., 2020). An
approach using decomposition workflow has shown to be one way to verify the chips (Kimura
et al., 2020). The approach also introduces a collection of testing bits that contains errors for
the verification process to unearth. The approach shows good performance in the verification
with a reduction to days down from months using other processes.
With the reliability of ICs being highly expected in modern chips, it creates a collection
of complexities that are hard to handle (Stempkovskiy et al., 2017). The researchers here use an
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approach of searching for faults in the logical circuits aiming to reduce the number of checks.
They achieve this by looking for one of each set of faults without repetitions for similar issues
(Stempkovskiy et al., 2017). Advancing technology is enabling additional verification approaches
for mixed function circuits (Martins et al., 2017). The challenge faced by these researchers is
the increased complexity of the multiple functions, which compounds the possible errors. The
approach takes a route to handle mixed-signal verification processes on the IC structures
(Martins et al., 2017).
Simatic et al. (2017) focuses on the design process of self-timed pipelines where the
proposed framework uses a mere and split flow to target the circuits. This approach also
performs high-level simulation for the design process, which is key in easing the costs of the
verification and tests. An additional approach to testing ICs through regression testing also
shows the promise of high performance (Cieplucha, 2019). The method is different as it
manages the test structures in a dynamic manner which constantly adjusts for performance
improvements during the simulation.
Hardware Verification and AI
The opportunities and possibilities presented by AI in verifying hardware during the ICs
design process are crucial for their utmost performance (Ogrutan & Dinu, 2019). The time-
consuming nature of the hardware verification step has pushed engineers and designers to look
for ways to reduce the time. AI is turning out to be the best option with its capabilities of
learning and adapting to new problems (Ogrutan & Dinu, 2019). Within the hardware
verification process, functional verification also takes a significant amount of time (Dinu et al.,
2022). Investments are currently heading towards using AI to reduce this time and make the
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process much quicker. Generic algorithms are shown to make it better for functional
verification when compared to the traditional approach (Dinu et al., 2022).
Current hardware verification approaches utilize the pseudo-random generation of
numbers to provide the necessary input (Gaur et al., 2019). The approach leads to a problem of
unexpected output, which in turn leads to unintended failures. The researchers propose a ML
model which traces the failures to the related inputs for the debugging process, which is
proving better. Additional research and the creation of new system architecture are also
expected to keep improving the verification process (Mittelsteadt, 2021). AI is intended to
cover the verification, assess performance, and improve the security of the chips.
Security concerns on malicious items being integrated into ICs by un-trustable
manufacturers are turning out to be a major concern (Dong et al., 2019). The existing
verification processes are also not capable of detecting these situations, with the option turning
out to be AI. The Internet of things is expected to rely on such AI-Based techniques for quick
and accurate verifications.
The scalability of the verification process is also an issue, as pointed out by (Shiraz &
Hasan, 2017). The issue occurs from the complexity of the process, which provides hurdles in
the mass verification of several designs. An approach to utilizing an AI-based library is showing
better capabilities.
IC Design Trends
Numerous areas are being integrated or created for ICs, including big data, AI, the
Internet of Things, Blockchain, robots, and more (Chan et al., 2022). These trends are leading to
an explosion in IC designs, and they need their customizations to handle the unique
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functionalities never experienced before. Innovators are also working hard for intriguing
designs that are best in precision, efficiency, and low on energy (Chan et al., 2022). Autonomy
and control in IC design are at their initial stages, but the innovation process is currently
underway (Li et al., 2022). The design of ICs is also concentrated in a small area of a few
manufacturers.
Sustainability in IC design is also an area to consider, especially for chips that utilize low
costs and can be manufactured at low costs (Kline et al., 2017). Some approaches are using IC
fabrication which targets the low energy consumption cases. Another approach is focused on a
multidisciplinary approach which aims to diversify the outcomes (Klinefelter, 2020). The use of
digital tools is an expected trend that is set to improve the general design of the chips. Open
sourcing, agile development, and more engineering and computer science expertise are also
slowly making the chip design process better (Klinefelter, 2020). Scaling the design process is
also a lucrative area, as it means more unique chip designs being rolled out in larger numbers
(Kahng, 2018). Deep and ML is set to take over the design and verification process resulting in
more efficient processes (Kahng, 2018).
IC Issue Detection
Designing resilient circuits that can detect and handle errors is a complex but necessary
task during the design stage (Lodhi et al., 2019). The testing and verification process is geared
toward running the chip in its worst case and determining its performance. Handling dynamic
problems is also critical to ensure the chips get the correct voltage and data and always process
information with high accuracy (Lodhi et al., 2019). Some processes utilize a patching
mechanism that handles the security problems on the chips (Liu et al., 2022). The result is
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flexible processes of the design process and ever-improving designs of the chips. The
researchers have also revealed that knowledge-based patching works better than random
searching for issues (Liu et al., 2022).
Anomaly detection is also an issue that Chen (2018) was able to handle through time
series forecasting. The prediction models work on the design data produced by the analog
design processes. The approach combines a predictive process that integrates into the issue
detection and can help raise the odds of success (Chen, 2018).
ML processes are also being utilized for detecting the issues, especially in the more
complex chip design processes (Khalid et al., 2020). The models help in detecting trojans hidden
in the hardware by observing the timing processes, power, communication patterns, and
current of the chips. The possibilities are endless of the capabilities the approach provides
(Khalid et al., 2020).
Scanning for issues on ICs, such as possible cyber-attacks, is turning out to be a vital
process (Vashistha et al., 2018). Trojans are increasing in their penetration, where they are
being observed at lower levels in circuits. The hardware trojans take attacks to a new level
where they are harder to handle and get rid of. Detecting trojans is currently difficult as there is
no universal approach to handling them (Vashistha et al., 2018). An approach by (Vashistha et
al., 2018) attempts to use a technique of using SEM imaging to look for evidence of trojans in
circuits. This approach utilizes ML and algorithms of vision to check changes in the circuits for
misbehavior. Hardware trojans are also being stated as a large problem that can be classified as
a war (Bhunia & Tehranipoor, 2018). Their devastation is higher than other software-based
trojan cases, which so far are easy to detect and handle. The research on trojans is increasing
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rapidly, gauged by the increasing cases of research targeting them. The workforce needed to
handle hardware trojans is reportedly huge (Bhunia & Tehranipoor, 2018). This difficulty comes
from the need for the testing and verification of the many steps involved in verifying circuits.
Trust issues are common during the creation of micro-electronics, where the increase in
complexity introduces a lot of participants for components and processes necessary (Hoque et
al., 2020). This complexity has led to many participants needing to specialize in crucial
components along the chain of manufacturing. For sensitive applications such as military cases,
trust is a crucial component of IC design and manufacturing (Hoque et al., 2020). The
appearance of hardware-based trojans on modern chips is also a testament to the issues faced
by their creation (Vashistha et al., 2021). The entire supply chain of manufacturing ICs has so far
been focused on improving methods to address the threats. Cases are being created where the
verification process can be confirmed and checked (Vashistha et al., 2021).
The physical inspection of ICs is possible and can detect design flaws in circuits
(Asadizanjani et al., 2021). Design flaws are based on the layout of the transistors and their
connections. The current testing process is noted to have flaws and introduces issues such as
noise in the circuits (Asadizanjani et al., 2021). The test patterns can be noted as insufficient
and hinder the capability of ICs. Validation process through physical inspections and
comparisons with the intended designs. Introducing the ML comparison mentioned previously
is one of the ways modernizations of circuit design is being implemented (Chen et al., 2022).
The assurance of hardware verification stands as a bottleneck in the design of circuits.
An additional approach to detect issues in IC design is the use of deep network
algorithms that learn the problems associated with the processes (Naswali et al., 2019). Deep
41
neural networks are different types of ML algorithms mimicking the learning process of
humans. The novel implementation by Naswali et al. (2019) shows an improvement to over
90% accuracy in detecting issues. The general reduction in the effort of the additional
confirmation process is 60%. The focus on these deep neural networks is also illustrated by
(Sharma et al., 2021) through the use of image classification. The test shows a reduction in the
noise in the image processing of more than 90%, pointing to a better approach to detecting
hardware errors. The increase of around 15% in better detection rate compared to existing
approaches is a testament to the capabilities of neural networks and their contribution to
better-ICs design (Sharma et al., 2021).
Gaps in the Literature
The need to improve hardware is pushing for ICs to be created for specific tasks
(Greenstein, 2021). Although there have been significant advancements in AI technology, the
development and production of integrated circuits remain a complex process, particularly as we
transition to smaller nanometer processes. Furthermore, microchips are expected to constantly
grow in complexity due to the ongoing advancements in semiconductor technology and the
increasing demand for higher performance and functionality in electronic devices.
As the semiconductor industry follows Moore's Law, which predicts that the number of
transistors on a microchip will double approximately every two years, the complexity of
microchips continues to increase exponentially. This continuous scaling of transistors enables
the integration of more computing power into smaller and more efficient chips.
Moreover, with the rise of technologies like artificial intelligence (AI), internet of things
(IoT), 5G, and autonomous vehicles, there is a growing need for microchips that can handle
42
complex tasks and process vast amounts of data quickly and efficiently. This demand drives the
development of more sophisticated microchips with specialized functions and improved
performance (Kapoor et al., 2019).
There is a gap between two areas that need to be bridged for machines to effectively
assess the situation, receive a requirement list, and efficiently design custom and synthesized
chips. This will reduce the need for vast human efforts and intervention during many time
consuming IC design stages. However, designers must still approve and sign off on the final
results generated by automation tools to make sure the desired outcome is achieved. The chips
of the future are also being pushed to be as low-cost as possible without compromising on their
performance (Myny, 2018). This reduction in cost allows for the implementation of affordable
computers and allows their mass production at lower budgets. However, the added complexity
makes the design and implementation process quite expensive for the latest design features to
be present. This gap means that there is a need to research alternative options for either
reducing the complexity or automating the process in a way that can be delegated to machines.
Having computers designed and offering options can be an excellent alternative to the existing
ones.
The EDA industry has incorporated AI technology within commercial, IC design,
verification, and simulation tools for over a decade. Leading companies like Cadence
(cadence.com), Synopsys (synopsys.com), and Siemens (siemens.com) are implementing
advanced AI and ML techniques within their software tools to address modern ICs' size, vast
amounts of data to analyze, complexity, and other design factors like design rules, thermal
43
analysis, and reliability. The continuous evolution of AI technology in this field is expected to
provide IC designers with further intelligent productivity enhancement tools.
Synopsys.ai is the first EDA solution suite to use AI from system architecture to
manufacturing, providing AI-driven solutions like digital design space optimization to achieve
power, performance, area targets, and boost productivity. Other EDA vendors are also
expanding the use of AI and machine learning to incorporate multiple tools, providing
continuity and access to consistent data at various points in the semiconductor design flow
(Talati & Saha, 2020).
Cadence Design Systems has been a leader in this area for many decades, incorporating
AI and machine learning techniques into its EDA tools for analog circuit synthesis, optimization,
and design exploration tasks (Cao et al., 2020). The Cadence Joint Enterprise Data and AI (JedAI)
Platform can harness the rich lode of EDA data in an open, AI-driven, large-scale data analytics
environment, allowing engineering teams to visualize the data, uncover hidden data trends,
and automatically generate design improvement strategies, leading to improved design
performance and engineering productivity. Using advanced AI techniques, Cadence EDA tools
are engineered to produce higher-quality ICs faster than ever. Implementing AI/ML technology
enables a significant boost in synthesis, verification, and simulation arenas.
Mentor, a Siemens Business, EDA tools use AI for tasks such as floor planning, place and
route, and timing optimization, enabling faster design iterations and better design quality. The
Siemens Xcelerator portfolio includes an AI and ML solution uniquely positioned to help
companies leverage AI- and ML-powered EDA tools to deliver differentiated AI- and ML-driven
innovations to market faster. The Siemens.ai EDA suite includes AI-driven solutions such as
44
digital design space optimization to achieve power, performance, and area targets and boost
productivity (A. Verma & Pedram, 2019).
Human skills still play a crucial role in microchip design and are considered superior to AI
in certain aspects, particularly in Analog, MIXED SIGNAL and RF designs. While AI and machine
learning techniques have made significant advancements in the field of microchip design and
are being increasingly integrated into the design process, human expertise remains essential for
complex and innovative designs (Wong & Mitra, 2019). Particularly Analog chip design involves
dealing with continuous signals and complex behaviors, which makes it more challenging for AI
to achieve the same level of expertise as experienced human designers. Analog and RF chip
designs often requires deep understanding, intuition, and creativity to address various
challenges, such as noise, parasitic effects, and variability. Human designers possess years of
experience and domain knowledge, allowing them to make informed decisions and design
circuits that meet specific performance requirements (Tajik & Samavati, 2019).
An AI can serve as a valuable tool for improving design productivity by automating time-
consuming tasks like layout optimization, placement, and routing. This can significantly reduce
design time and enhance efficiency. However, the expertise of human designers is often
required to make critical decisions, creatively solve problems, and push the boundaries of
microchip performance and power efficiency. Designing advanced microchips involves dealing
with complex trade-offs between performance, power consumption, area, and cost. It requires
a deep understanding of circuit design principles, materials, and manufacturing processes.
Human designers possess intuition, creativity, and domain knowledge that enable them to
45
make informed decisions and come up with novel solutions that may not be achievable through
automated AI algorithms alone (Cao et al., 2020).
These gaps identify the areas of focus of this study as the progress in AI and IC design is
determinant of the emerging technologies in the near future. This study will focus on looking
for the automation capabilities of AI in IC design forming the baseline of the needed changes in
technology.
Conclusions
The literature shows that AI is a quickly growing area with a lot of possibilities and
implementation options (Dauvergne, 2021). The integration of AI technology in the world of IC
design has marked significant progress, promising breakthroughs in performance, automation,
and efficiency. However, when it comes to tackling the complexities of advanced nanometer
nodes, AI faces challenges related to perception, performance, and capabilities. The
continuous shrinking of technology nodes has enabled the fabrication of advanced nanometer
ICs with unprecedented complexity and performance. The design and optimization of such
chips demand advanced methodologies, and AI has emerged as a promising solution to meet
these requirements (Bhardwaj et al., 2020). AI has transformed various stages of IC design,
including layout synthesis, physical design, verification, and optimization. Neural networks,
machine learning algorithms, and reinforcement learning techniques have shown remarkable
progress in automating repetitive tasks, exploring design spaces, and accelerating time-to-
market.
With all modern electronic devices being powered by ICs, it is turning out to be
important to concentrate on the efficiency of designing and creating them. The IC design
46
process is a complex area that AI is currently helping to streamline (Fujita, 2019). It is clear that
the increasing complexity of IC design requires the formation of AI based frameworks and
models that help ease the process.
AI technology is driving advancements in IC design, but there are still challenges in
dealing with advanced nanometer nodes ICs related to perception, performance, and
capabilities. As the design and manufacturing of microchips present new challenges based on
the laws of physics, the need for faster and more effective EDA tools is expected to grow.
Circuit design is becoming more complex, manufacturing nodes design rules are getting smaller,
and there is a demand for low power. These factors create many opportunities for AI
technologies in the IC design domain (S. Liu et al., 2020).
AI models face challenges in accurately perceiving the intricacies of designs at advanced
nanometer nodes. Factors like process variations, electromigration, and parasitic effects
introduce uncertainties that AI must account for in its predictions. AI-driven tools are currently
encountering performance limitations when dealing with large-scale IC designs, high-
dimensional design spaces, and computationally intensive simulations. Hardware accelerators
and distributed computing may be necessary to address these challenges effectively (Jain &
Zhai, 2021). The capabilities of AI tools must align with the unique requirements of advanced
nanometer nodes. Design exploration, yield enhancement, and power optimization demand
sophisticated AI algorithms capable of handling complex IC architectures. AI models must adapt
to the rapidly evolving IC design landscape. A continuous learning approach ensures that AI
algorithms stay up-to-date with the latest process technologies and design methodologies (Sun
et al., 2021).
47
One important point to consider is that despite significant progress, AI still lags behind
human experts in some areas that require intuition to solve (Liu & Chakrabarty, 2021).
Nonetheless, AI is improving rapidly and could become a dependable tool in the near future.
Despite the challenges, the potential of AI technology in IC design remains promising. As
AI algorithms evolve, they are expected to overcome the limitations and redefine the landscape
of IC design for advanced nanometer nodes.
Chapter Summary
The process of organizing the literature review was taken in stages, beginning with an
overview of the main topics of AI and IC design. A deeper analysis of each of the sections was
then followed with a highlight of their relation to the problem of the study. A final section on
the gaps in the literature and the concluding observations were made to bring together the
literature.
AI is swiftly altering the human approach to problem-solving in many different frontiers
(J. Lee et al., 2021). Computing can take advantage of AI at the core level in the design of chips.
The identified issue that currently needs improvement is the verification stage (Fujita, 2019).
This stage is currently mostly manually performed by engineers introducing a bottleneck
(Miranda, 2020). Early ML and AI models are being developed to help counter this problem. The
need for easier and more efficient design processes for custom chips is also rising. These chips
are often complex and are susceptible to attacks through hardware-based Trojans (Vashistha et
al., 2018). The next stage in computer improvement is identified as speeding up the design
process for more diverse machines. The identified gaps are in easing the design process without
48
compromising quality, introducing low-cost solutions, and combining different technological
approaches for better solutions in chip design. This paper further explores the AI and IC design
subjects through a methodological approach outlined in the coming chapter. The following
chapter after the literature review is the methodology section which outlines the steps the
dissertation is taking to explore the research questions.
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Chapter 3: Methodology, Design and Methods
This chapter describes the approach used to complete the study by gathering the
necessary information and analyzing it. The purpose of the qualitative exploratory research was
to explore the disconnection between AI applications and IC design. The problem lay with the
high costs of designing chips, difficulties in the verification of IC circuits due to the complexity of
the process, and limitations in the automation of the verification process, which modern
technology had yet to achieve but intended to be handled by AI (Kovacs et al., 2020). The
conceptual framework explored the major topics of AI and ICs, dissecting them and combining
different subtopics to reach a conclusion investigating the raised issues. This detailed approach
was covered by the qualitative study outlined below, which states the study's paradigms,
philosophy, steps, and considerations.
Research Methodology and Design
The research utilized the qualitative approach, which involved looking into non-
numerical information to obtain insights from it. The qualitative research targeted handling the
IC design problem through existing or upcoming methods in AI. The choice of the qualitative
approach leveraged the nature of the topic and the intent of the study. The topics involved a
few theoretical philosophies on approaching circuit design with additional contributions of
possible improvements. Using the qualitative approach was necessary to match up the
phenomena behind problems associated with integrated circuit design. The Likert scale
questions helped provide a quantitative viewpoint of the participant’s views of the topic areas.
An additional reason for the qualitative study was to investigate the existing problems and
attempt to provide insights or answers regarding more efficient IC design. Qualitative research
50
approaches also aim to simplify the complexity of a subject and provide newer viewpoints on
the issues (Martinez & Valverde, 2022). The relation of this research to the Doctor in Computer
Science program was that computing is constantly evolving as newer approaches are realized.
Frequent review of the existing solutions and the possibilities of the new ones allows upcoming
solutions to be created. AI and ML are the current new frontiers that are slowly maturing and
helping to improve new IC design approaches (Mirabbasi et al., 2023).
The research design chosen, the exploratory design, was driven by the paradigmatic
interpretive perspective. This interpretive perspective and exploratory design are used to dig
into a topic and provide the necessary information to enlighten and derive meaning from it as
thoroughly as possible (Doyle et al., 2020). Looking at the need to reveal the issues surrounding
IC design and AI, it was fitting to utilize the exploratory design to explain the two topics. The
results came as a summary that reveals the shortcomings and areas needing improvement. The
sources for the data included a questionnaire targeting the area of focus.
As noted, the research design selected for this study was the exploratory design. This
approach aims to look deeper into the progress so far on a topic of interest. For this research,
the topics are AI and ICs design, and which analysis was needed to move to the next revolution
of computing. The exploratory approach provided the framework to guide the data gathering
and help in extracting the knowledge, expanding it, and bumping up the progress in the topics.
This qualitative exploratory approach was justifiable because it combined the existing
approaches and views and consolidated them while adding new views and possible solution
approaches (Swedberg, 2020). This approach ensures that the contribution of the two areas is
51
represented and the main themes revealed. These themes allowed the formulation of the
additional themes that continue the story and allow the continuation of the story.
Population, Sample, and Participant Recruitment
The population of the study was the experts connected to the design of computer chips
through modern approaches. This population covered the practitioners in the computing fields
who partook in creating better-performing integrated circuits. The choice for the population
was to understand their impacts and knowledge progress in improving computing through
approaches like machine learning and artificial intelligence. Due to the technical nature of the
topics being studied, the population for the research had to be limited to individuals with
significant experience and important positions. Therefore, a small group was selected for the
study. The size of the target population is hard to estimate given the highly technical nature of
the area. This study assumes about 50,000 population, with more than 25 years of experience
in the USA.
The target sample of the study was a set of eight experts familiar with artificial
intelligence and integrated circuit design. The reason for narrowing down the type of
participants was that the areas of study were narrow and quite technical, and a randomized
group of participants would not possess the necessary knowledge for a meaningful outcome.
This group of participants was intended to provide their expert view and knowledge on the
research questions and contribute to its objectives.
The choice for participants was individuals who have dealt directly or indirectly with the
designing of integrated circuits practitioners in the artificial intelligence and machine learning
fields and possible researchers who were concerned with the progress of AI and IC design with
52
at least five years of experience. These characteristics made up the necessary target population
and sample group to provide adequate knowledge and allow for an expansion by studying their
input.
The intended sampling approach was to use the purposive sampling method. This is a
non-probabilistic sampling approach that utilizes participants who best fit the study (Indrayadi,
2020). The reason for choosing this approach is that the area of study was quite narrow and
requires a high set of specialized skills. There was a need to identify the possible candidate and
request them for participation in the study, then administer the information-gathering process.
Accessing the sample was not easy, given that the target area is a niche area. Further,
the target participants often find it hard to find time owing to their busy lives. The confidence
for the access of the sample, therefore, was medium with the intention to use boosting
approaches through snowball. Following Institutional Review Board approval, the starting point
for recruiting the participants was my personal network of experts in the field. These were
contacted via phone calls to inform them of the intention of the study and enquire about their
participation. The goal was to have a sample size of eight participants for the survey. The phone
calls informing the participants were also followed up by an email with the invite link to the
consent form. Filling out the form and confirming the details then prompted the sending of the
invite link to fill up the survey for the participants to fill out as needed.
Data Collection Instrumentation and Procedures
The collection approach and instrument in the study was a questionnaire. The choice of
this collection instrument was to gather the sentiments and viewpoints of the participants in
53
relation to the topic. The usage of the questionnaire in a qualitative study was motivated by the
time restrictions available.
A qualitative study can use a questionnaire as one of its data collection methods, but it
is not the most common approach. Qualitative research primarily relies on in-depth and open-
ended methods such as interviews, observations, focus groups, and content analysis of texts or
documents to gather rich and detailed data. These methods allow researchers to explore
complex phenomena, gain a deeper understanding of participants' perspectives, experiences,
and behaviors, and generate detailed narratives. However, questionnaires can be used in
qualitative research as a supplementary data collection tool in certain situations (Creswell &
Poth, 2016). For this study, I chose a questionnaire that enabled the collection of data through
both open-ended and closed-ended survey questions. This approach efficiently gathered the
participants' viewpoints and knowledge. This instrument is commonly used in qualitative
studies within the field of computer science (Kamal et al., 2020; Powe II, 2019). Consent of the
participants was first required before commencing each session to gather their views. The text
filled in the questionnaires was key to interpreting and comparing the results of the
participants. This study aimed to receive the most honest opinions on AI and IC design in the
modern world. Integrated circuits (ICs) are at the heart of many modern electronics, powering
everything from smartphones and computers to medical devices and automobiles (James et al.,
2022). IC design is a complex and time-consuming process, requiring expertise in circuit design,
simulation, and layout. As of 2023, AI has been employed to design ICs, promising to improve
the efficiency and speed of the design process. AI technology is often used to design modern
integrated circuits design due to its substantial benefits. The participants, therefore, were
54
encouraged to be as detailed as possible and try to keep their responses unbiased. The choice
of the questionnaire was to allow as much time as possible for the participants to internalize
the questions and answers based on their experiences and knowledge. This means that they
could also pause and resume the answering at a future date as long as it was before the
deadline for providing the answers. Raw data points were also available for the replicability of
the study.
After signing the consent form, the participants were then provided with a link to
connect to the Google Forms platforms for the questions (see Appendix A). This link is
redirected to Google Forms with the survey questions, which the participants had to go through
and answer. Each of the unique IDs of the participants was used for identification purposes. The
data was temporarily held in the Google account containing the form pending the filling of the
participants. This data was then exported for further analysis through Excel.
Data Analysis Procedures
The initial step covered the gathering of the survey responses into a dataset for easy
analysis. While Google Forms provided minimal tools to create visualizations, the dataset
needed exporting for deeper analysis. Data exploration and cleanup were then conducted on
the data to understand its structure and rectify any mistakes available (Foxwell & Foxwell,
2020). The exploration also involved the creation of additional visualizations to better
understand the responses.
The text mining and analysis process covered the translation of the information
obtained in the research so far into meaningful code for easy interpretation (Richards &
Hemphill, 2018). The process took place in two stages, where an initial set of interpretations
55
and codes were applied and then refined to the final code in the last stage. The coding process
was manually handled with support and help from Microsoft Excel. The results were
interpreted to get the general knowledge contributed by the participants and its relation to
computing in general. Qualitative analysis to determine the main themes, patterns, insights,
and knowledge from the data was conducted. This allowed the triangulation of the results to
pinpoint the validity of the knowledge about the research. Finally, these insights were merged
into a collection of insights and summary points on the research for the final reporting process.
Trustworthiness
Trustworthiness in the research provides affirmation of why the proceeds and the
results should be regarded as important to expand the knowledge. Trustworthiness generally
covers four main areas, which include credibility, transferability, dependability, and
confirmability (Kyngäs et al., 2020).
Credibility in this study is assured by sourcing the information from areas that are
known to produce truthful information (Self & Roberts, 2019). Going through the general
contribution of the involved information allowed the differentiation of credible sources, with
the ones not trusted eliminated. The participants providing the responses were required to be
highly skilled in the area where they provided the information. The questionnaire and the
picking of the participants concentrated on the experience level of each participant to back up
their contribution. The study also involved triangulation to support its credibility. The process
covered the combination of sources and approaches to improve the level of credibility of the
study (Noble & Heale, 2019). The process further involved the comparisons of the gathered
responses in a bid to improve the understanding of the researcher to make credible additions
56
to AI and IC design. To use triangulations effectively, the researchers gathered data from the
survey of multiple participants and combined it with the existing literature gathered so far on
the topics.
The information and results collected from the questionnaires have to be easily
transferable to the study to reveal insights (Kyngäs et al., 2020). Ensuring this characteristic was
enforced by keeping the questions straight and to the point and the topics specific. This eased
the complexity of the information and reduced the chances of ambiguity during the process of
transferring the data. The population targeted in the study were the industry experts handling
the design of IC chips. Additional targeted experts were also those who utilized artificial
intelligence and machine learning to solve problems. This means that the contribution was
expected to expand their knowledge while gathering what they understand so far. The research
was also set to utilize the latest technology in gathering and analyzing the data to convert it to
knowledge and for use in making conclusions.
Dependable data is necessary for expanding the topic areas. Ensuring dependability is
addressed by linking the study with relevant data on the topic areas (Sumrin & Gupta, 2021). All
steps are also outlined in the process conducted without hidden critical choices. Openness
ensures that the study can be investigated and reproduced as needed. The research also
ensured that the data was constant and not altered in any way by limiting the people
interacting with it. The gathering tools were also limited, with only access to the researcher to
view and interact with the responses. The procedure of transferring was also keen to keep the
data in its original state by limiting the path of transfer between different tools.
57
To allow the study to be confirmable, the measures taken need the adequate linking of
relevant and accurate sources (Stenfors et al., 2020). Tracing the information and verifying it
ensured that it is as reliable as possible. The researcher intended to be neutral at all times when
interacting with the responses. Any contribution from the participants was interpreted as is and
included in the conclusions without change. The trail of contribution was also recorded for any
auditing necessary after the data gathering was done. Bias was eliminated, and any piece of
data was removed from the analysis and interpretations.
Ethical Assurances
Protecting the participants was performed by handling the collection process as private.
The research did not collect personal information and placed the participants in a position
where they would not provide their views freely. Any sensitive information that might be
captured was redacted and removed from the collected dataset. The participants also had the
freedom to back out of the data-gathering at any point in the collection process. The
participants had to consent to the study before starting the entire process.
This consent was relevant to the Belmont report in that it provided informed consent,
outlined the risks and benefits, and had a fair selection of the participating subjects (Sims,
2010). The study was set to maintain the high respect of the people contributing to its
knowledge base. Their contribution was acknowledged and highly respected by thanking them
and following up on the impact of their contributions. The research also intended to be and
uphold laws and rights for the participants and those affected by the results. The researcher
had a moral obligation to ensure the best outcome for the participants and the research. This
call for beneficence made sure that the core part of the research and the researcher was to do
58
good by expanding knowledge and contributing to problem-solving. The research also benefited
society in that it sought better problem-solving through improved computers.
Data will be stored for a maximum of seven years through encrypted archives only
accessible to the researcher. For permanence, the data was replicated after encrypting to
copies of the archives and future retrieval if possible. The research was also reviewed by the
CTU Institutional Review Board to ensure that it keeps up with its claims and adheres to high
ethical standards.
Chapter Summary
This chapter has indicated the method applied to the dissertation. The study focused on
using a qualitative approach through an interpretivist paradigm. This framework allowed the
study to provide a narrative of the topic areas, seeking the gaps and outlining possible
solutions. The study described the topic in detail and revealed the available issues. The
instruments used were a survey through a questionnaire. Raw data was provided for the
replication process, with results presented through summaries.
The trustworthiness of the study was ensured by using reliable sources, especially skilled
participants in the relevant professions. The study also ensured dependability by being straight
to the point and providing enough details for easy interpretation and understanding. All
participants were taken through a process to consent and given room to back out on
uncomfortable areas.
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Chapter 4: Findings
This chapter handles the presentation of the results of the study. To recap, the purpose
of this qualitative exploratory research was to explore the disconnection between AI
applications and IC design. The major problem lies with the high costs of designing chips,
difficulties in the verification of IC circuits due to the complexity of the process, and limitations
in the automation of the verification process, which modern technology is yet to achieve but
intended to be handled by AI (Kovacs et al., 2020).
Description of the Study Sample
The population of the study was the experts connected to the design of computer chips
through modern approaches. They ranged in age from 48 to 65 years old. Four participants
were from the USA. One participant was from Canada. Three participants were from Israel.
Years of experience in computing ranged from 28 to 39 years.
This population covered the practitioners in the computing fields who partook in
creating better-performing integrated circuits. This population was relatively small, given the
technical nature of the topics. The estimated size of the target population was roughly 50,000
worldwide, given its complexity and niche status.
The target sample of the study was a set of eight experts familiar with artificial
intelligence and integrated circuit design. The reason for narrowing down the type of
participants was that the areas of study were narrow and quite technical, and a randomized
group of participants would not possess the necessary knowledge for a meaningful outcome.
This group of participants was intended to provide their expert view and knowledge on the
research questions and contribute to its objectives.
60
The choice for participants were individuals who have dealt directly or indirectly with
the designing of integrated circuits practitioners in the artificial intelligence and machine
learning fields and possible researchers who are concerned with the progress of AI and IC
design with at least 5 years of experience. These characteristics make up the necessary target
population and sample group to provide adequate knowledge and allow for an expansion by
studying their input.
The sampling approach used was the purposive sampling method. This is a non-
probabilistic sampling approach that utilizes participants who best fit the study (Indrayadi,
2020). The reason for choosing this approach is that the area of study is quite narrow and
requires a high set of specialized skills. There is a need to identify the possible candidate and
request them for participation in the study, then administer the information-gathering process.
The final sample size used for the study was eight participants, with the choice coming
from the similarity of the responses to provide a clear and meaningful contribution. These were
all experts with decades of experience in artificial intelligence and integrated circuit design. All
eight participants were male and above 45 years of age. This means that they were people with
experience in computing. The years of experience were all above 25 years, with the most being
39 years in the industry. The participants were from USA, Israel, and Canada. It is also notable
to point out that all the participants had knowledge of computing and integrated circuits. In the
latter area of focus, the participants also had experience and knowledge of the verification
process in circuit design.
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Results
The questionnaire posed 20 questions to elicit the expertise of the participants to
address the central research question for the study, how can AI be implemented within IC
design, and if so, what are the options? The questions asked are listed below:
1. How do you rate yourself on understanding AI?
2. How do you rate yourself on understanding IC design?
3. What level of disruption is likely to occur to IC design from AI?
4. How will this disruption occur if you think it will happen?
5. How relevant is Moore's law today?
6. Is Moore’s law dead in your opinion? Why So?
7. What level is AI likely to influence IC design in the future?
8. What is the likelihood of AI-based IC design overtaking engineer-based IC
design?
9. How will IC design change over time in the coming future?
10. Do we currently need customized chips to fully support AI and ML? Why so? (For
your choice above)
11. How useful is the contribution of AI in developing better computer designs
across:
1. Custom chips?
2. More powerful computers?
12. How likely is AI to provide solutions for IC design?
13. Do you have knowledge of the verification process of IC design?
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14. How likely will AI help improve the verification process of IC design?
15. How likely are AI approaches to take over IC design verification?
16. In a few words, what is your opinion on AI improving IC design?
17. What are the key areas necessary to integrate AI automation in IC design?
18. How likely is it to keep advanced IC design costs low? How so? (Feedback)
19. Do we generally need so many powerful computers? Why?
20. Do you have any other relevant information/insights about AI Technology
involvement within future IC design?
The insight per question is presented to follow. Each question result is presented in
sequential order to demonstrate the different levels of complexity applied in the study. The
questions progressed from general ones to more detailed ones and thus preferred to be
presented in their sequential order. The information from the questions not presented in the
results was integrated into the results and the interpretations, as their nature makes it hard to
report them out rightly. This knowledge was integrated to ensure the continuous and smooth
reading of the paper.
Technological Disruption
Question 4 asked, how do you anticipate the world’s technological disruption to occur if
you think it will happen? The general consensus regarding the oncoming disruption through
technology is that computers will get more advanced, especially in artificial intelligence. This
perspective is demonstrated by the response, “As new ICs are much more complex to design, it
becomes more challenging to design and manufacture modern ICs. The field will need more
automation software advancements, which will be definitely AI based.” The participants pointed
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out that AI is rising rapidly alongside the complexity of integrated chips. Chips are also
becoming smaller, as reported by one of the participants, with their integration spreading to
almost all areas of life. Designs are also set to get more complex and result in more complex
computers that might be beyond our current understanding.
The Relevance of Moore’s Law
Question 5 asked, how relevant is Moore's law today? And question 6 asked, in your
opinion, is Moore’s law still a standard in the IC arena? Explain? Five of the eight participants
reported that Moore's law is still relevant at the present time at a ranking of high likelihood.
The remaining three reported a neutral view of Moore's law being relevant currently. The views
from the participants indicated that Moore's law still exists but not in the sense it was first
defined. As an example, one participant responded, “Not sure. Technologists no longer shrink
the transistors the same way they did in the first decades since Moore introduced his law. The
geometry scaling has now different meaning.” The participants are of the view that transistors
are no longer shrunk in the manner that they were in early computing days. Other participants
observed that the transistors are more than doubling every 2 years, while others think that the
law has finally reached its peak.
The Future of IC Design
Question 7 asked, what level is AI likely to influence IC design in the future? Seven of the
eight participants agree that the likelihood of the future of IC design being influenced by AI is
high. One participant had a neutral standpoint on the matter.
AI-based Design
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Question 8 asked, what is the likelihood of AI-based IC design overtaking engineer-based
IC design? The participants had a divided view on the likelihood of AI overtaking engineers in
designing integrated circuits. 5 of the eight rates the takeover as likely and very likely. One
participant had a neutral standpoint on the takeover, while the remaining two see that the
takeover is unlikely.
IC Design Change
Question 9 asked, how will IC design change over time in the upcoming future?
Integrated circuit design changes over time seem to be getting more complicated, as indicated
by six of the eight participants. According to the participants, these chips are set to include
more features in computation. One noted, “IC design and manufacturing will have to include
more advanced technologies in order to make more complex chips.” The participants also
pointed out that the chips are set to use less power despite their increase in complexity. Their
integration with AI also seems to be a direction some participants pointed towards. Automation
will also be more widespread as these chips become tinier and more complicated. However,
two participants pointed out that the changes will not be as drastic as anticipated. The already
tiny 3nm circuits are almost at their physical limit, and there might be no more advances.
Customized Chips
Question 10 asked, do we currently need customized chips to fully support AI and ML
technologies? Seven of the eight participants agree that we need customized chips to support
AI and machine learning technology fully, highlighting things like, “It is probably proven
performance gain over use non- specialized chips. I mean, there could be the data to support
it.”, and “To achieve high computing power, keeping design time lines in a reasonable range,
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maintain accuracy and compliance with complex deep nanometer design rule complexities. “.
They pointed out that more complexity and higher computing power cannot be solved by
existing chip designs. The participants also noted that custom chips are more optimized for
handling specific tasks yielding better results. Adjustments in the architecture of the chips is a
notable change that the participants agree needed to happen to accommodate the needs.
However, one participant disagreed and pointed out that it is not necessary for the changes.
AI and Better Microchips
Question 11 inquired about the level of contribution of using AI in the development of
improved microchips, which was measured through nine options in Figure 2. Different
technologies are seen to be of varying importance in introducing AI to IC design. It appears that
most participant agree that AI will have a significant impact on Synthesis, SoC, ASICs, and
Memory ICs. This is because these fields rely heavily on IC Design automation. However, there
is less predicted involvement of AI technology in Analog, MEMS, Photonics, and other areas.
This is likely because these domains require more human expertise that is based on extensive
experience and knowledge. The varying levels, as presented, show that there is a high
likelihood of newer ways to create microchips in the coming future.
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Figure 2
Question 11 Results.
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AI Solutions for IC Design
Figure 3
Question 12, 14, and 15 results
In Figure 3, there is analytics provided for questions 12, 14, and 15. These questions
were designed to collect information on how AI technologies will impact the IC design field in
the future. The results show that participants are in agreement that AI will have a positive
impact, allowing for advanced solutions and significant design improvements.
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Question 12 asked, how likely is it for AI technologies to provide solutions for IC design?
Concerning the likelihood of AI providing solutions to IC designs, six of the participants agreed
that it is likely for AI technologies to provide solutions for integrated circuit design. The
remaining two had neutral standpoints on such contributions.
On investigating the likelihood of AI improving the verification process (Question 14),
the participants also universally agreed that artificial intelligence contributes to improving the
verification process in integrated circuit design.
Question 15 asked, how likely are AI approaches to take over IC design verification?,
seven of the eight participants agreed that it is very likely that AI-based approaches will take
over the verification of integrated circuits. The participants pointed out that the complexity
might be too high for an engineer to carry out, as pointed out by one participant said: “AI is the
only way to verify a deep nanometer IC of 5nm and below. It will be used for functional
verification, physical verification, simulation and manufacturing aspects like DFM analysis”.
These changes are expected to happen in the near future as AI will easily go through the
existing approaches and find the best-fitting ones for different tasks. One participant
highlighted that it is unlikely for AI to take over. He pointed out regarding AI that, “It can
suggest improvements that will need to be verified by an Engineer”.
Question 17 asked, what are the key areas necessary to integrate AI automation in IC
design? and the participants quoted that it varies from the architecture, the verification
processes, validation, physical design, and manufacturing. One said, “Physical verification,
Simulation, functional verification, Design For Manufacturing (DFM) analysis, Reliability
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Verification (RV), DRC, LVS”, and another pointed out the areas as “Manufacturing, high level
architecture, functional verification, physical verification, DFM considerations.”
Question 18 asked, how likely is it to keep advanced IC design costs low using AI
Technology? The participants observed that the design costs for integrated circuits have a high
likelihood of being kept low through AI, as six of the participants indicated. These opinions
came from the understanding that AI technology will improve automation and require fewer
human resources and costs. The remaining two were neutral regarding the improvement of the
costs through AI.
Additional Opinions on AI
Question 19 asked, how do you see the involvement of AI techniques within Quantum
Computing ICs? The participants observed that the complexity contributed by AI is expected to
be huge in regard to quantum computing. One participant said, “Definitely. Due to the
complexity of Quantum Computing process, AI is most likely the only way to perform it. AI is the
only technology that will enable vast, intelligent computing power and resources distribution
efficiency which is a key in Quantum Computing”. Five of the eight participants did not have
knowledge or had neutral thoughts on AI having a significant contribution to quantum
computing.
Question 20 asked, do you have any other relevant information/insights about AI
Technology involvement within future IC design? The sentiments were that it is set to be
common for the computer creation process. Some participants are of the view that this
technology is set to dominate computing in the near future, saying “AI will become a common
technology for IC design firms. EDA tools will incorporate them in many ways to increase IC
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design’s performance, productivity and accuracy.”, and “AI will eventually be able to
automatically design microchips from beginning to end. We will reach the situation of
computers-making-computers.”
Overarching Findings
While participants agreed that AI is set to change computer design, there was a divided
opinion on whether AI will likely be a revolutionary change in chip design or not. Despite the
lack of consensus on whether AI will be revolutionary to changes in chip design, the aggregated
results from the study indicate several key findings.
IC design keeps getting complex with the increasing demand for computer tasks. The
complexity is prompting the inclusion of AI to help in designing the chips. Upcoming concepts
such as deep learning are set to improve the design process and will soon take over the design
processes.
AI will also bring about far-reaching changes, such as improving automation, excelling in
repetitive tasks, solving problems requiring high computing, and pointing out possible
solutions.
There are physical limitations to chip design and improvements are being worked on
through artificial intelligence. Further, the design processes might be currently at their
maximum operations needing a technological breakthrough.
Discussion of Study Findings
Through the analysis of the data collected from the study participants, the key findings
emerged to include AI is unavoidable, automation is needed to improve the performance of IC
design, Moore’s law might be on its end times, and AI is set to be valuable in many of the
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processes in IC design. Through these findings, the central research question was addressed.
The central research question for this study asked, how can AI be implemented within IC
design, and if so, what are the options? The theme of AI being valuable to many of the
processes in IC design provided insight into the options for the question. AI is set to help in
suggesting solutions, helping in the development of chips, processors, verifications, IC
integration, and more. The theme of AI being unavoidable illustrates that technology is a major
change that infiltrates all areas. It is clear that AI will help in changing our approach to IC design
and will provide useful insights and alternative viewpoints.
The general agreement by the participants that AI is set to change the realm of
computer design shows that it is here to stay. This is supported by Li et al. (2019) observation of
AI being a major changing point in technology. However, there was also the theme of some
skeptical sentiments among some participants who view that technology might be overhyped.
Therefore, there was a divided opinion on whether AI will likely be a revolutionary change in
chip design or not.
Chapter Summary
The contribution of the participants was a good one as far as AI and IC design are
concerned. The participants had good knowledge and experience in the area, which was
required by the study. This knowledge was also highlighted in the quality of the answers, which
were good. The purpose of the study was to look at the options available in AI and IC design
was well represented by the participants upon observing the results. The implications of the
study findings are evaluated in the following chapter.
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Chapter 5: Discussion and Conclusions
This chapter highlights the discussion of the presented results and provides a conclusion
to the study. The purpose of this qualitative exploratory research is to explore the
disconnection between AI applications and IC design. The problem lies with the high costs of
designing chips, difficulties in the verification of IC circuits due to the complexity of the process,
and limitations in the automation of the verification process, which modern technology is yet to
achieve but intended to be handled by AI (Kovacs et al., 2020).
Limitations of Study Findings
One of the key limitations is that the chosen participants were all experienced and had
decades-long interactions with computing. While this was an added advantage to the study, it
means that the likelihood of bias in their views was high. Continued working in the industry
means that they may be more likely to be resistant to any changes. This can be supported by
the hesitance to acknowledge AI as a replacement and consider it a helper technology. Having
such a limitation in mind is necessary for interpreting the sentiments provided by the
participants. A wider range of participants would have been better, but challenging to identify
and reach out given the highly technical nature of the area of study.
Another limitation of the study is towards challenges in the sampling process. It was
hard to find new experts in the field to partake in answering the questions. This left the study
with highly experienced participants only. The balance needed for a study was not
accomplished in this case. The gap was filled by going through other studies and including their
knowledge in this paper and incorporating the interpretations of the participants with the
gathered information. The study is still useful despite this limitation, as the literature review
73
covers the gap by including updates on information that complements the contribution of the
participants.
The sample size of eight participants might also have been small for the study. A wider
number of participants would cover more areas and provide a more detailed view. However, a
small sample size is appropriate for qualitative exploratory studies (Shaheen & Pradhan, 2019).
Data saturation was observed in the participant responses. This small sample size is also a key
part of knowledge to have when going through the paper and trying to understand the
interpretations. However, the study is still beneficial in that few participants had valuable
knowledge with decades of experience. This means that their contribution was deemed
necessary in the changes upcoming in AI and IC design.
Interpretation of Study Findings
The responses from the participants make it clear that artificial intelligence is changing
integrated circuit design. This means that it is a technology that will stay around for a long time
and influence the approach to creating computers in the future. This view is confirmed as
highlighted by Berente et al. (2021) illustrated in the literature. One of the ways it is set to help
in IC design is by suggesting solutions during the design process.
Despite the lack of consensus on whether AI will be revolutionary to changes in chip
design, the aggregated results from the study indicate several key findings, including the
unavoidability of AI, the improvements in IC design, and Moore’s law being towards its end.
Another finding is that AI will bring about far-reaching changes, such as improving automation,
excelling in repetitive tasks, solving problems requiring high computing, and pointing out
possible solutions. AI will improve solutions by looking for vast datasets and gathering
74
intelligence while comparing multiple designs for the best ones. For customization, the multiple
designs will be useful in tuning specific areas of the circuits to fit the needs. Monroe (2018)
confirms this integration of AI into IC designs for custom situations. AI is also set to help in the
verification of designs which is considerably a bottleneck in the current processes.
Computerizing this process is expected to introduce a whole new way of viewing the design of
integrated circuits. Automation is also a strong candidate for the contribution of AI in IC design.
Most processes are repetitive and would fit well into automation. Adding AI means that the
automation will be intelligent and improve over time as issues are discovered and solved.
Current automation is limited to robotics, and introducing AI is among the top needs for
manufacturing processes. AI is also good at offering alternative viewpoints. Its interaction with
knowledge bases means that it can gather information and formulate alternative ways to
handle issues. The suggestions will be good in challenging designers and lead to better circuits.
A good consideration is that artificial intelligence is a helper to design experts. Its
development is not advanced enough to completely replace them. The design experts also have
human intuition in handling the designs, a feature that AI might be lacking. This is in contrast
with the observation by Nardi et al. (2019) that AI could replace most positions through
automation. Incorporating processes that utilize the expertise of the designers and the
capabilities of AI will therefore lead to better circuits. This is promising for fields where custom
chips are necessary and where human expertise and AI will play well. The changes are also set
to introduce more possibilities for research in the area and further extend the capabilities of
computing.
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Regarding Moore's law, it is observed that it is no longer applicable as much as modern
changes are concerned. Transistors in chips are now too dense to continue doubling every two
years. The current level of density of transistors has also surpassed the expectations of the law.
This invalidates it with regard to its original view and interpretation. The widespread availability
of powerful small devices is a testament to Moore's law, where circuits have gotten small and
heavy computation is possible in limited spaces. A considerable view is that such power might
be at its peak, given the limitations in the capabilities of the density of the transistors.
Practice Implications of Study Findings
This study reveals that AI is capable of helping out in integrated circuit design. This help
comes in the form of working out efficiencies in repetitive tasks or suggesting improvements in
the design process. These contributions imply that AI should be integrated into IC design as
soon as possible to start helping out in the process. Integration could involve experiments to try
out the suggested improvements while monitoring the performances. AI is also an area that
requires a lot of testing in several phases, and starting out translates to results being
implemented in the near future. Documenting the results of the experiments can also help in
iterations on the improvement of the design processes aided by AI.
So far, the progress in computing and chip design has introduced a wide range of
possibilities for creating integrated circuits. Each of these design approaches has its own
upsides and downsides. Having a way to collect the design processes and feed them into an AI
system can be a start in commencing the improvements. Since AI works best when trained with
large datasets and allowed to seek better approaches, using these design processes can be a
76
good start. The system can also do well with input on how these designs have changed and
what is expected, and how the design processes have contributed.
The study's revelations on the capabilities of AI point to the needed bridging of
computing with other fields. With the needed custom chips for different activities, it would
require input from both ends to design the perfect chips. This is also an area where AI is set to
contribute to by analyzing these custom needs and incorporating the knowledge in the designs.
One action is to open up the research on AI and other fields to as many individuals as
possible. One recurring theme in technological revolutions is the existence of intensive research
prior to the implementation of the technologies. Such research ensures that all theoretical
possibilities are explored before the experimentation starts on the capabilities of the
technology. For IC design, expanding the research to the non-technical fields is set to help
reveal the possibilities, problems, and how each of the areas complements each other. Better
solutions are, therefore, expected when the research area is as accessible as possible to people
contributing.
Researcher Reflections
Looking at the study reveals key takeaways that have shaped the answers it intended to
find. For instance, the intricate nature of IC design is a key part of driving computing. Circuits
are complex objects that need attention to detail and require many processes. The view
towards integrated circuits has changed over the course of this research study and highlighted
their critical nature. The existence of circuits in many electronic devices goes to show how
these designs affect everyone. It is also important to highlight that IC design needs experts
during the creation process. This clears up the doubt present on whether AI is to replace the
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designers. The complexity of the circuits is too much to be currently handled by AI, which points
to the fact that their input will remain invaluable for a long time to come. Integrated circuits,
therefore, make up the core of computing, and designing them is the start of their usefulness.
Regarding artificial intelligence, there is a clear view that it still has a long way to go. The
initial view was that it was nearly taking over computing tasks. This view has so far been
updated to that AI still has some evolution to get to a state where it can be dominant. The state
of AI is that of a helping technology, and it is evolving to be more complex. This also brings the
view to AI being unstoppable in most tasks. Tasks requiring repetition, knowledge from large
sets, and alternative viewpoints will benefit greatly from AI. The insight from the participant has
helped reshape the notion that AI will remain as a limited helper to humanity. Its contribution
to computing alone is enough to change the view towards our interaction with computers. AI-
powered chips and machines, therefore, are about to be implemented more.
Recommendations for Further Research
There is a need to research more into artificial intelligence and integrated circuit design.
This research reveals that there is more to these two areas which can be explored and which
should be handled through an exploratory qualitative study. The research can look into both
the theoretical and practical aspects of the areas.
Another recommendation is to look into consolidating the integrated circuits design
knowledge. The limited information currently on the area shows that it can take a lot of work to
seek its knowledge. Its consolidation can help fuel further research onto how they work and
how they can be improved. This is possible through an extensive literature review on the fields.
78
A quantitative study into the extent of the IC designs and how well they can be
improved by AI is also needed. Research can help in revealing how much work has been
accomplished so far and what is needed for the next leap in revolution, paving the way for
more.
The last recommendation is towards the nature of IC designs. There is a need to look at
the options available in automating IC design. This phenomenon was beyond the scope of this
study and is needed in future research. It can help reveal the possibilities and what steps need
to be taken through a qualitative study.
Conclusion
The purpose of the qualitative exploratory research is to explore the disconnection
between AI applications and IC design. The problem lies with the high costs of designing chips,
difficulties in the verification of IC circuits due to the complexity of the process, and limitations
in the automation of the verification process, which modern technology is yet to achieve but
intended to be handled by AI (Kovacs et al., 2020). The research question centers on looking for
the ways that AI can combine with IC design to handle the problems experienced by modern
design processes. It is, 'How can AI be implemented within IC design, and if so, what are the
options?'. The research utilizes the qualitative approach, which involves looking into non-
numerical information to obtain insights from it. The qualitative research targets handling the
IC design problem through existing or upcoming methods in AI.
The observations of this study are quite applicable to the computing world. For instance,
the observed growth of AI means that designers and engineers need to keep improving on the
technology and find ways to integrate it. AI is here to stay, and early preparations will see
79
solutions created as soon as needed. The crucial nature of engineers and experts in IC design
also means that they have work to do. It is clear that having more experts contributing to
integrating AI will be better and lead to well-performing machines. Experts have an obligation
to work in creating the needed knowledge base for creating AI-based chips. They also need to
work with experts in other fields to create custom chips in the coming future. The highlighted
areas where AI is set to help in IC design include physical verification, validation of the designs,
the design process itself, and manufacturing and automation tasks. Some of these tasks also
have repetitive processes which will fit the AI integration and will see to better designs.
The recommendations show that there is a need to research more into artificial
intelligence and integrated circuit design. This research reveals that there is more to these two
areas which can be explored and which should be handled through exploration. The research
can look into both the theoretical and practical aspects of the areas. Another point is to look
into consolidating the integrated circuits design knowledge. The limited information currently in
the area shows that it can take a lot of work to seek its knowledge. Its consolidation can help
fuel further research into how they work and how they can be improved. A quantitative study
into the extent of the IC designs and how well they can be improved by AI is also needed.
Research can help in revealing how much work has been accomplished so far and what is
needed for the next leap in revolution, paving the way for more. There is also a need to look at
the options available in automating IC design. This phenomenon was beyond the scope of this
study and is needed in future research. It can help reveal the possibilities and what steps need
to be taken.
80
The future of AI technology within the IC Design field holds immense potential for
transformative advancements and paradigm shifts. As AI continues to evolve, its integration
into IC design processes is expected to bring about significant improvements in efficiency,
performance, and innovation. AI-driven design automation will likely streamline complex tasks,
such as logic synthesis, placement, and routing, reducing design time and human intervention.
This will pave the way for faster and more cost-effective IC development. Moreover, AI's
predictive capabilities will play a pivotal role in optimizing IC designs for performance and
power efficiency (Ghosh et al., 2020). Machine learning algorithms will enable precise modeling
and fine-tuning of circuit behavior, leading to highly reliable and energy-efficient
semiconductor solutions. The IC Design world will also witness the benefits of AI in analog and
mixed-signal circuit design, where AI-driven simulations and optimizations will enhance
accuracy and reduce design iterations. Design verification, traditionally a time-consuming
process, will be bolstered by AI's coverage analysis and bug detection capabilities, ensuring
high-quality and dependable ICs (Mittal & Vrudhula, 2019).
AI's ability to explore vast design spaces will foster creativity and ingenuity, unlocking
novel solutions and pushing the boundaries of what is achievable in IC design. While the future
of AI in IC Design is promising, it also raises the importance of addressing challenges related to
data security, ethics, and interpretability. Ensuring the robustness and trustworthiness of AI
models will be crucial to building reliable and safe ICs. As the synergy between AI and IC Design
grows stronger, collaboration among experts in AI, semiconductor technology, and electronic
design automation (EDA) will be pivotal to unleash the full potential of AI-driven innovations.
The future of AI technology within the IC Design field is poised to revolutionize the
81
semiconductor industry, empowering engineers with powerful tools, accelerating design cycles,
and ushering in a new era of efficiency, reliability, and cutting-edge IC solutions (Cao et al.,
2018). With responsible and innovative AI implementation, the IC Design world is on the brink
of a transformation that will shape the technology landscape for years to come.
82
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Appendix A: Questionnaire
Demographics
Age (Years)
Gender
Male
Female
Other
Location
Experience in Computing (Years)
1. How do you rate yourself on understanding AI?
Very high
High
Neutral
Low
Very low
2. How do you rate yourself on understanding IC design?
Very high
High
Neutral
Low
Very low
AI & IC Design
3. What level of disruption is likely to occur to IC design from AI?
Very high
High
Neutral
Low
Very low
4. How will this disruption occur if you think it will happen?
5. How relevant is Moore's law today?
Very high
High
Neutral
Low
Very low
6. Is Moore’s law dead in your opinion? Why So?
7. What level is AI likely to influence IC design in the future?
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Very high
High
Neutral
Low
Very low
8. What is the likelihood of AI-based IC design overtaking engineer-based IC design?
Very likely
Likely
Neutral
Unlikely
Very unlikely
9. How will IC design change over time in the coming future?
10. Do we currently need customized chips to fully support AI and ML?
Yes
No
10.1 Why so? (For your choice above)
11. How useful is the contribution of AI in developing better computer designs across:
11.1 Custom chips?
Very high
High
Neutral
Low
Very low
11.2 More powerful computers?
Very high
High
Neutral
Low
Very low
12. How likely is it for AI to provide solutions for IC design?
Very likely
Likely
Neutral
Unlikely
Very unlikely
13. Do you have knowledge of the verification process of IC design?
Yes
No
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14. How likely will AI help improve the verification process of IC design?
Very likely
Likely
Neutral
Unlikely
Very unlikely
15. How likely are AI approaches to take over IC design verification?
Very likely
Likely
Neutral
Unlikely
Very unlikely
16. In a few words, what is your opinion on AI improving IC design?
17. What are the key areas necessary to integrate AI automation in IC design?
18. How likely is it to keep advanced IC design costs low?
Very likely
Likely
Neutral
Unlikely
Very unlikely
18.1 How so? (Feedback)
19. Do we generally need so much powerful computers? Why?
20. Do you have any other relevant information/insights about AI Technology
involvement within future IC design?
