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Memory consolidation and synaptic learning. a) Schematic diagram of the biological memory consolidation process in human brain. Channel conductance change (∆G) as a function of time with different b) light pulses numbers, c) light pulse widths, and d) light pulse intensities.
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2D organic semiconductors (OSCs) with atomically layered scaling structure have been attracting intensive attention in recent years. Benefiting from their unique size advantages, 2D materials have the potential to be immune to short‐channel effects. High‐performance photoresponsive transistors based on 2D OSC films with excellent light‐stimulated s...
Citations
... Consequently, the methodology of fabricating synaptic devices inspires researchers globally to engage in the development of artificial synapses that interconnect neurons. [8][9][10] Numerous optoelectronic synaptic devices featuring dual, triple, and multi-terminal configurations have been documented for the purpose of emulating neurobiological synapses. [11][12][13][14] As the biological synapses have two terminal structures, a twoterminal device is preferable to replicate the neurobiological synapses. ...
... 34 The regulation of this chargetrapping phenomenon upon the light excitations helps to replicate the basic neurobiological functionalities. 10 Different semiconductor materials, such as oxide, organic, inorganic, and 2D materials, have been widely used as active channel materials for fabricating photo synaptic transistors. [34][35][36][37] Among them, organic semiconductors have the advantage of solution processibility, disorderliness, low-cost processing, and a relatively high absorption coefficient that helps to build a photo synaptic transistor. ...
... The transfer characteristics exhibit hysteresis behavior, which can be attributed to the presence of traps at the gelatin/PBTTT-C14 interface, indicating the memory-storing capability of the OFETs. 10 The decrease in hysteresis behavior under light illumination conditions is due to the modulation of trap charge carriers. 50 The trapped charge carriers are released under illumination conditions, causing an increase in the conductivity of the channel layer. ...
In recent times, there has been a growing interest in the development of light-stimulated artificial synapses for applications related to artificial intelligence. Low operating voltage, low energy consumption, less noise, high transmission rate, and high operating speed make artificial synaptic organic field-effect transistors (OFET) suitable candidates for future artificial complex neural network development. In this work, we demonstrate human cognitive activities through the utilization of water-soluble gelatin biopolymer gated poly(2,5-bis(3-alkylthiophen-2-yl)) thieno [3,2-b] thiophene [PBTTT-C14] synaptic OFETs. The devices exhibit basic neurobiological phenomena including excitatory post-synaptic current (EPSC), pair-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), STP to LTP conversion, and learning-forgetting-memorizing (LFM) nature similar to the human brain. The photo-response parameters such as photoresponsivity, detectivity, and the photo and dark current ratio are estimated for the better realization of the photo synapses. The OFETs exhibit commendable photoresponsivity of 11.10 mA W⁻¹, high detectivity of 8.55 × 10⁸ Jones, and the photo to dark current ratio of 654. Moreover, these photo synaptic OFETs imitate the human emotion-tuneable and the mood-swing-influencing (MSI) memory and learning behavior. Further, We also demonstrate the implementation of the ‘OR’ logic gate under the stimulation of two different wavelengths by utilizing these OFETs. Additionally, we replicate Pavlov's dog experiment to explain the associative learning nature of the OFETs. The OFETs exhibit a fairly low energy consumption of ∼89 nJ per optical event to perform basic neurobiological activities which facilitates the development of complex artificial neural networks with minimal energy consumption.
... A gradual re-capturing process by localized trap sites occurs in the absence of illumination, leading to a steady decline in photocurrent magnitude. 38,39 Owing to the excellent photoresponse performance of the device, we conducted further investigations into the optical synaptic stimulation. Figure 3(a) shows the schematic of a biological synapse. ...
The light-induced synaptic transistors, with their large-scale and cost-effective benefits, hold significant promise for advancing neuromorphic electronics. In this work, we propose a hybrid phototransistor with a channel layer composed of C8-BTBT and PM6. This device exhibits an extended optical response range in comparison to pure C8-BTBT transistors. In addition, the device shows excellent synaptic plasticity under red, green, and blue light stimuli, with the potential for tuning through light dosage and pulse duration. The study further confirms consistent device performance and reliable operation. Moreover, we show that this type of device can be fabricated into array to write the letters “C”, “S”, and “U” and store red, green, and blue information. These experimental results show the excellent responsiveness and storage performance of our devices under red, green, and blue light stimuli, suggesting promising applications in artificial vision.
... Therefore, simulating the biological functions of synapses is the key to developing neuromorphic computing. In recent years, various structures and principles of electronic synaptic devices have been proposed, such as memristors [21][22][23][24][25][26], phase change memory [27][28][29][30][31], resistive-switching memory [32,33], and ferroelectric transistors [34][35][36]. Despite significant progress, electronic synapses remain limited in terms of operating speed, bandwidth density, and interconnectivity, given their typically pure electronic inputs and outputs. ...
Neuromorphic systems represent a promising avenue for the development of the next generation of artificial intelligence hardware. Machine vision, one of the cores in artificial intelligence, requires system-level support with low power consumption, low latency, and parallel computing. Neuromorphic vision sensors provide an efficient solution for machine vision by simulating the structure and function of the biological retina. Optoelectronic synapses, which use light as the main means to achieve the dual functions of photosensitivity and synapse, are the basic units of the neuromorphic vision sensor. Therefore, it is necessary to develop various optoelectronic synaptic devices to expand the application scenarios of neuromorphic vision systems. This review compares the structure and function for both biological and artificial retina systems, and introduces various optoelectronic synaptic devices based on different materials and working mechanisms. In addition, advanced applications of optoelectronic synapses as neuromorphic vision sensors are comprehensively summarized. Finally, the challenges and prospects in this field are briefly discussed.
... On the other hand, recent studies have been reported in which the photogating effect further plays a role as a key mechanism of application. In particular, it began to be applied in neuromorphic devices [131][132][133][134] and optoelectronic memory [135][136][137], focusing on the photogating effect and the PPC behavior. ...
Rather than generating a photocurrent through photo-excited carriers by the photoelectric effect, the photogating effect enables us to detect sub-bandgap rays. The photogating effect is caused by trapped photo-induced charges that modulate the potential energy of the semiconductor/dielectric interface, where these trapped charges contribute an additional electrical gating-field, resulting in a shift in the threshold voltage. This approach clearly separates the drain current in dark versus bright exposures. In this review, we discuss the photogating effect-driven photodetectors with respect to emerging optoelectrical materials, device structures, and mechanisms. Representative examples that reported the photogating effect-based sub-bandgap photodetection are revisited. Furthermore, emerging applications using these photogating effects are highlighted. The potential and challenging aspects of next-generation photodetector devices are presented with an emphasis on the photogating effect.
... Artificial optoelectronic synapses can be divided into four types -heterojunction channel (HC)-typed synapses [20][21][22][23], oxide semiconductor-based synapses [14,24], floating gate-based synapses [25,26], and circuit-based synapses [27,28]. Among them, HC-typed synapses have introduced additional laser absorbers (such as perovskites [29][30][31], chalcogens [32][33][34], and organic semiconductors [35]) to form a heterojunction with high-mobility semiconductors in the channel region, showing highly sensitive photoconductance properties [36]. ...
... As shown in Fig. 3g, the half-life period of the photocurrent decay is 35.59 s after illumination by a 1.90 W/cm 2 laser pulse. Compared with the periods in reported devices (Table 1) [14,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], the GNWs/CsPbBr 3 QDs synapses have both a long memory time and a microampere-level photocurrent, owing to the combined effect of the MGB and the built-in electric field formed at the GNWs/CsPbBr 3 QDs interface. A complete neuromorphic computing system requires the integration of the processor and many other electronic circuits. ...
The rapid development of neuromorphic computing has stimulated extensive research interest in artificial synapses. Optoelectronic artificial synapses using laser beams as stimulus signals have the advantages of broadband, fast response, and low crosstalk. However, the optoelectronic synapses usually exhibit short memory duration due to the low lifetime of the photo-generated carriers. It greatly limits the mimicking of human perceptual learning, which is a common phenomenon in sensory interactions with the environment and practices of specific sensory tasks. Herein, a heterostructure optoelectronic synapse based on graphene nanowalls and CsPbBr 3 quantum dots was fabricated. The graphene/CsPbBr 3 heterojunction and the natural middle energy band in graphene nanowalls extend the carrier lifetime. Therefore, a long half-life period of photocurrent decay - 35.59 s has been achieved. Moreover, the long-term optoelectronic response can be controlled by the adjustment of numbers, powers, wavelengths, and frequencies of the laser pulses. Next, an artificial neural network consisting of a 28 × 28 synaptic array was established. It can be used to mimic a typical characteristic of human perceptual learning that the ability of sensory systems is enhanced through a learning experience. The learning behavior of image recognition can be tuned based on the photocurrent response control. The accuracy of image recognition keeps above 80% even under a low-frequency learning process. We also verify that less time is required to regain the lost sensory ability that has been previously learned. This approach paves the way toward high-performance intelligent devices with controllable learning of visual perception.
... These negative charges induce a gate effect, which leads to a continuous hole injection and maintains a highly conductive current state.3.2.2.3. Dielectric/OSC InterfaceThe interfacial charge-trapping effect between dielectrics and semiconductors is widely used as an effective strategy to implement photosynaptic transistors.Many studies using various organic small molecules, including C8-BTBT[69,70,73] and pentacene[62,71,[74][75][76][77], have been reported. Owing to the high mobility and stability of C8-BTBT, a photonic synapse using C8-BTBT as a channel and a polyacrylonitrile (PAN) film as a dielectric layer has been demonstrated (Fig. 3(a))[69]. ...
Organic photonic synapses are promising candidates for optoelectronic neuromorphic electronic components owing to their advantages from both material and signal perspectives. Organic materials have advantages such as low cost, tunable properties according to the molecular design, mechanical flexibility, and biocompatibility. In addition, using light as an input signal affords advantages such as ultrafast signal transmission speed, wide bandwidth, and wireless communication. Thus, different types of organic photonic synapses have been researched using various mechanisms and new materials. In this review, we first introduce the biological synaptic properties imitated by photonic synapses. Next, the operating mechanism and materials used are discussed by categorizing the device structures into two-terminal and three-terminal devices. To verify the applicability of organic photonic synapses in the real world, we present various applications such as pattern recognition, smart windows, and Pavlov’s dog experiment, which have been demonstrated in previous studies. Finally, we discuss the remaining challenges and provide directions for further research on organic photonic synapses.
... [54][55][56][57][58][59] In the three-terminal artificial synapses, there exist two fundamental components: the semiconducting channel and gate dielectric layer. For semiconducting channels, the most common organic semiconductors include pentacene, 60 benzothiophene (C 8 -BTBT), 61 and poly(3-hexylthiophene) (P3HT), 62 beneficial for their process maturity and continuity of large-scale film formation. As for 2D materials, such as graphene, 63 MoS 2 , 64 and WSe 2 , 65 can also be employed as the semiconducting channel materials in diverse fields due to their larger atomic utilization, large specific surface, and high charge transport ability. ...
The physical implementation of artificial neural networks, also known as “neuromorphic engineering” as advocated by Carver Mead in the late 1980s, has become urgent because of the increasing demand on massive and unstructured data processing. complementary metal-oxide-semiconductor-based hardware suffers from high power consumption due to the von Neumann bottleneck; therefore, alternative hardware architectures and devices meeting the energy efficiency requirements are being extensively investigated for neuromorphic computing. Among the emerging neuromorphic electronics, oxide-based three-terminal artificial synapses merit the features of scalability and compatibility with the silicon technology as well as the concurrent signal transmitting-and-learning. In this Perspective, we survey four types of three-terminal artificial synapses classified by their operation mechanisms, including the oxide electrolyte-gated transistor, ion-doped oxide electrolyte-gated transistor, ferroelectric-gated transistor, and charge trapping-gated transistor. The synaptic functions mimicked by these devices are analyzed based on the tunability of the channel conductance correlated with the charge relocation and polarization in gate dielectrics. Finally, the opportunities and challenges of implementing oxide-based three-terminal artificial synapses in physical neural networks are delineated for future prospects.
... Therefore, it is crucial to mimic synaptic functions to implement neuromorphic computation systems with proper devices. Artificial synaptic devices, in which the synaptic weight can be controlled using external stimuli, have been reported to perform basic synaptic functions [6,7]. ...
To utilize continuous ultralow intensity signals from oxide synaptic transistors as artificial synapses that mimic human visual perception, we propose strategic oxide channels that optimally utilize their advantageous functions by stacking two oxide semiconductors with different conductivities. The bottom amorphous indium–gallium–zinc oxide (a-IGZO) layer with a relatively low conductivity was designed for an extremely low initial postsynaptic current (PSCi) by achieving full depletion at a low negative gate voltage, and the stacked top amorphous indium–zinc oxide (a-IZO) layer improved the amplitude of the synaptic current and memory retention owing to the enhancement in the persistent photoconductivity characteristics. We demonstrated an excellent photonic synapse thin-film transistor (TFT) with a precise synaptic weight change even in the range of ultralow light intensity by adapting this stacking IGZO/IZO channel. The proposed device exhibited distinct ∆PSC values of 3.1 and 18.1 nA under ultralow ultraviolet light (350 nm, 50 ms) of 1.6 and 8.0 μW/cm2. In addition, while the lowest light input exhibited short-term plasticity characteristics similar to the “volatile-like” behavior of the human brain with a current recovery close to the initial value, the increase in light intensity caused long-term plasticity characteristics, thus achieving synaptic memory transition in the IGZO/IZO TFTs.
... As a result, additional components are required to offer the future transistor-based synapses with persistent conductance switching dynamics and analogue conductance updating capabilities. Fang et al recently published a light-stimulated artificial synaptic device based on 2D OSC deposited at room temperature using the solution epitaxy approach [151]. Because of its outstanding solubility and crystallinity, the p-type organic small molecule 2,7-dioctyl benzothieno [3,2-b] benzothiophene (C 8 -BTBT) was used as the synapse's channel layer. ...
Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.
... In von Neumann architecture, instruction and program data are stored as separate memory units, which are not appropriate way to solve complex problems. On the other hand, the human brain, consisting approximately ~ 10 11 neurons and ~ 10 15 synapses, can parallelly perform these complex learning, forgetting, thinking, audio/ visual recognition, and movement control [3]. In addition, the power consumption of the human brain (~ 20 W) is about one millionth of the operating speed of supercomputer based on von Neumann architecture (10 7 W) [4,5]. ...
Two-terminal memristive devices are considering as a potential candidate to mimic human brain functionality to enable artificial intelligence. Particularly, two-terminal nanoscale devices are regarded as a promising solution for implementing bio-synapses due to their small dimensions, extremely compact, and low power to operate neuromorphic functions. Here, we demonstrate that the nanoscale charge transport and resistive switching behavior of VOx thin film can be tuned by modulating the substrate morphology. Particularly, the device prepared with flat-Si shows totally distinguished behavior in comprising of reactive ion-etched-Si substrates. Interestingly, conductive atomic force microscopy current maps revealed the electric field inhomogeneity due to a change in substrate morphology. A reliable bipolar resistive switching behavior of the corresponding etched devices have been demonstrated. Due to an increase in the etching time of substrate, an increase in active area and decrease in work function was observed. Further, nanoscale synaptic functions were generated from the corresponding devices, showing a strong conduction path at preferential bright spots of the particular devices. Moreover, finite element simulations confirm the modulation in generation of localized current conduction in particular etched devices by applying tip voltages. These findings represent a new way to generate nanoscale artificial synaptic functions.
