Akash Levy

Stanford University | SU · Department of Electrical Engineering

HfO₂-based resistive RAM (RRAM) is an emerging nonvolatile memory technology that has recently been shown capable of storing multiple bits-per-cell. The energy/delay costs of an RRAM write operation are dependent on the number of pulses required for RRAM programming. The pulse count is often large when existing programming approaches are used for m...

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning with Stanford Associative memory for Programmable, Integrated Edge iNtelligence via life-long learning and Search (SAPIENS), a resistive random access memory (RRAM)...

We propose the use of multi-pole nanoelectromechanical (NEM) relays for routing multi-bit signals within a coarse-grained reconfigurable array (CGRA). We describe a CMOS-compatible multi-pole relay design that can be integrated in 3-D and improves area utilization by 40% over a prior design. We then demonstrate a method for placing multiple contact...

We present the first demonstration of 1T4R Resistive RAM (RRAM) array storing two bits per RRAM cell. Our HfO2-based RRAM is built using a logic foundry technology that is fully compatible with the CMOS back-end process. We present a new approach to program RRAM cells using gradual SET/RESET pulses while minimizing disturbances on adjacent cells (b...

In this dissertation, I present techniques for improving the power, performance, and area of integrated circuits (ICs) through 3-D integration of two emerging nanotechnologies: (1) resistive random-access memory (RRAM), a non-volatile memory with multiple-bits-per-cell storage capability, and (2) nanoelectromechanical (NEM) relays, nano-scale mecha...

Designing compact and energy-efficient resistive RAM (RRAM) macros is challenging due to: 1) large read/write circuits that decrease storage density; 2) low-conductance cells that increase read latency; and 3) the pronounced effects of routing parasitics on high-conductance cell read energy. Multiple-bits-per-cell RRAM can boost storage density but...

High-frequency wideband Hall effect sensors can measure currents in power electronics that operate at higher frequencies (in MHz range). We experimentally investigated the frequency limit of Hall effect sensor designs based on a 2-dimensional electron gas (2DEG) gallium arsenide/aluminum gallium arsenide (GaAs/AlGaAs) heterostructures. For the firs...

Data stored in the cloud or on mobile devices reside in physical memory systems with finite sizes. Today, huge amounts of analog data, e.g. images and videos, are first digitalized and then compression algorithms (e.g., the JPEG standard) are employed to minimize the amount of physical storage required. Emerging non-volatile-memory (NVM) technologi...

After the passage of the U.S. National Quantum Initiative Act in December 2018, the National Science Foundation (NSF) and the Office of Science and Technology Policy (OSTP) recently assembled an interagency working group and conducted a workshop titled “Key Concepts for Future Quantum Information Science Learners” that focused on identifying core c...

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning using a 2T-2R resistive RAM (RRAM) non-volatile associative memory (AM) as the backend of memory-augmented neural networks (MANNs). The 64-kbit fully integrated RRA...

Two-terminal Resistive Random-Access Memory (2T-RRAM) devices have been researched extensively for high density memory and neuromorphic computing applications. Recently, a three-terminal variant (3T-RRAM) of this device family has been investigated experimentally, which, by separating the read and write terminals, seeks to enable efficient resistiv...

In this work, we explore deep learning methods to perform time series prediction on petroleum well output. We successfully trained restricted Boltzmann machines (RBMs), fully-connected networks (FCNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) on petroleum well data to accomplish this task. A comparison of our resul...

Oxide nanoelectronics is a rapidly growing field which seeks to develop novel materials with multifunctional behavior at nanoscale dimensions. Oxide interfaces exhibit a wide range of properties that can be controlled include conduction, piezoelectric behavior, ferromagnetism, superconductivity and nonlinear optical properties. Recently, methods fo...

A two dimensional electron gas has recently been demonstrated at the interface between amorphous Al2O3 and TiO2-terminated SrTiO3 by atomic layer deposition (ALO/STO).[2] Similar to LaAlO3/SrTiO3 heterostructrues, when the ALO thickness exceeds a critical thickness, the interface becomes conducting. By using a conducting atomic force microscope tip...