Recent Progress in Three-Terminal Artificial Synapses: From Device to System

Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems

The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.

One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System

The field of biomimetic electronics that mimic synaptic functions has expanded significantly to overcome the limitations of the von Neumann bottleneck. However, the scaling down of the technology has led to an increasingly intricate manufacturing process. To address the issue, this work presents a one-shot integrable electropolymerization (OSIEP) method with remote controllability for the deposition of synaptic elements on a chip by exploiting bipolar electrochemistry. Condensing synthesis, deposition, and patterning into a single fabrication step is achieved by combining alternating-current voltage superimposed on direct-current voltage-bipolar electropolymerization and a specially designed dual source/drain bipolar electrodes. As a result, uniform 6 × 5 arrays of poly(3,4-ethylenedioxythiophene) channels are successfully fabricated on flexible ultrathin parylene substrates in one-shot process. The channels exhibited highly uniform characteristics and are directly used as electrochemical synaptic transistor with synaptic plasticity over 100 s. The synaptic transistors have demonstrated promising performance in an artificial neural network (NN) simulation, achieving a high recognition accuracy of 95.20%. Additionally, the array of synaptic transistor is easily reconfigured to a multi-gate synaptic circuit to implement the principles of operant conditioning. These results provide a compelling fabrication strategy for realizing cost-effective and disposable NN systems with high integration density.

Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors

Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

Memristive Switching Mechanism in Colloidal InP/ZnSe/ZnS Quantum Dot-Based Synaptic Devices for Neuromorphic Computing

Quantum dots (QDs) have garnered a significant amount of attention as promising memristive materials owing to their size-dependent tunable bandgap, structural stability, and high level of applicability for neuromorphic computing. Despite these advantageous properties, the development of QD-based memristors has been hindered by challenges in understanding and adjusting the resistive switching (RS) behavior of QDs. Herein, we propose three types of InP/ZnSe/ZnS QD-based memristors to elucidate the RS mechanism, employing a thin poly(methyl methacrylate) layer. This approach not only allows us to identify which carriers (electron or hole) are trapped within the QD layer but also successfully demonstrates QD-based synaptic devices. Furthermore, to utilize the QD memristor as a synapse, long-term potentiation/depression (LTP/LTD) characteristics are measured, resulting in a low nonlinearity of LTP/LTD at 0.1/1. On the basis of the LTP/LTD characteristics, single-layer perceptron simulations were performed using the Extended Modified National Institute of Standards and Technology, verifying a maximum recognition rate of 91.46%.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

Spacer Engineering Enables Fine-Tuned Thin Film Microstructure and Efficient Charge Transport for Ultrasensitive 2D Perovskite-Based Heterojunction Phototransistors and Optoelectronic Synapses

2D Ruddlesden-Popper phase layered perovskites (RPLPs) hold great promise for optoelectronic applications. In this study, a series of high-performance heterojunction phototransistors (HPTs) based on RPLPs with different organic spacer cations (namely butylammonium (BA+), cyclohexylammonium (CyHA+), phenethylammonium (PEA+), p-fluorophenylethylammonium (p-F-PEA+), and 2-thiophenethylammonium (2-ThEA+)) are fabricated successfully, in which high-mobility organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b]benzothiophene is adopted to form type II heterojunction channels with RPLPs. The 2-ThEA+-RPLP-based HPTs show the highest photosensitivity of 3.18 × 107 and the best detectivity of 9.00 × 1018 Jones, while the p-F-PEA+-RPLP-based ones exhibit the highest photoresponsivity of 5.51 × 106 A W-1 and external quantum efficiency of 1.32 × 109%, all of which are among the highest reported values to date. These heterojunction systems also mimicked several optically controllable fundamental characteristics of biological synapses, including excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory states. The device based on 2-ThEA+-RPLP film shows an ultra-high PPF index of 234%. Moreover, spacer engineering brought fine-tuned thin film microstructures and efficient charge transport/transfer, which contributes to the superior photodetection performance and synaptic functions of these RPLP-based HPTs. In-depth structure-property correlations between the organic spacer cations/RPLPs and thin film microstructure/device performance are systematically investigated.

Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High-k Oxide Composite Dielectric

Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

Piezo strain-controlled phase transition in single-crystalline Mott switches for threshold-manipulated leaky integrate-and-fire neurons

Electrical manipulation of the metal-insulator transition (MIT) in quantum materials has attracted considerable attention toward the development of ultracompact neuromorphic devices because of their stimuli-triggered transformations. VO2 is expected to undergo abrupt electronic phase transition by piezo strain near room temperature; however, the unrestricted integration of defect-free VO2 films on piezoelectric substrates is required to fully exploit this emerging phenomenon in oxide heterostructures. Here, we demonstrate the integration of single-crystalline VO2 films on highly lattice-mismatched PMN-PT piezoelectric substrates using a single-crystal TiO2-nanomembrane (NM) template. Using our strategy on heterogeneous integration, single-crystal-like steep transition was observed in the defect-free VO2 films on TiO2-NM-PMN-PT. Unprecedented TMI modulation (5.2 kelvin) and isothermal resistance of VO2 [ΔR/R (Eg) ≈ 18,000% at 315 kelvin] were achieved by the efficient strain transfer-induced MIT, which cannot be achieved using directly grown VO2/PMN-PT substrates. Our results provide a fundamental strategy to realize a single-crystalline artificial heterojunction for promoting the application of artificial neurons using emergent materials.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

Lateral heterostructures of WS2 and MoS2 monolayers for photo-synaptic transistor

Von Neumann architecture-based computing, while widely successful in personal computers and embedded systems, faces inherent challenges including the von Neumann bottleneck, particularly amidst the ongoing surge of data-intensive tasks. Neuromorphic computing, designed to integrate arithmetic, logic, and memory operations, has emerged as a promising solution for improving energy efficiency and performance. This approach requires the construction of an artificial synaptic device that can simultaneously perform signal processing, learning, and memory operations. We present a photo-synaptic device with 32 analog multi-states by exploiting field-effect transistors based on the lateral heterostructures of two-dimensional (2D) WS2 and MoS2 monolayers, formed through a two-step metal-organic chemical vapor deposition process. These lateral heterostructures offer high photoresponsivity and enhanced efficiency of charge trapping at the interface between the heterostructures and SiO2 due to the presence of the WS2 monolayer with large trap densities. As a result, it enables the photo-synaptic transistor to implement synaptic behaviors of long-term plasticity and high recognition accuracy. To confirm the feasibility of the photo-synapse, we investigated its synaptic characteristics under optical and electrical stimuli, including the retention of excitatory post-synaptic currents, potentiation, habituation, nonlinearity factor, and paired-pulse facilitation. Our findings suggest the potential of versatile 2D material-synapse with a high density of device integration.

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

Reliable synaptic plasticity of InGaZnO transistor with TiO2interlayer

We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2buffer layer. The structure of the synaptic transistor with TiO2inserted between the Ti metal electrode and an IGZO semiconductor channel O2trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

High-Endurance Long-Term Potentiation in Neuromorphic Organic Electrochemical Transistors by PEDOT:PSS Electrochemical Polymerization on the Gate Electrode

The brain exhibits extraordinary information processing capabilities thanks to neural networks that can operate in parallel with minimal energy consumption. Memory and learning require the creation of new neural networks through the long-term modification of the structure of the synapses, a phenomenon called long-term plasticity. Here, we use an organic electrochemical transistor to simulate long-term potentiation and depotentiation processes. Similarly to what happens in a synapse, the polymerization of the 3,4-ethylenedioxythiophene (EDOT) on the gate electrode modifies the structure of the device and boosts the ability of the gate potential to modify the conductivity of the channel. Operando AFM measurements were carried out to demonstrate the correlation between neuromorphic behavior and modification of the gate electrode. Long-term enhancement depends on both the number of pulses used and the gate potential, which generates long-term potentiation when a threshold of +0.7 V is overcome. Long-term depotentiation occurs by applying a +3.0 V potential and exploits the overoxidation of the deposited PEDOT:PSS. The induced states are stable for at least 2 months. The developed device shows very interesting characteristics in the field of neuromorphic electronics.

A bi-functional three-terminal memristor applicable as an artificial synapse and neuron

Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks

Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10-7 V, and achieve an ultra-low energy consumption of 2.14 × 10-18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

Progress, Challenges, and Opportunities in Oxide Semiconductor Devices: A Key Building Block for Applications Ranging from Display Backplanes to 3D Integrated Semiconductor Chips

As Si has faced physical limits on further scaling down, novel semiconducting materials such as 2D transition metal dichalcogenides and oxide semiconductors (OSs) have gained tremendous attention to continue the ever-demanding downscaling represented by Moore's law. Among them, OS is considered to be the most promising alternative material because it has intriguing features such as modest mobility, extremely low off-current, great uniformity, and low-temperature processibility with conventional complementary-metal-oxide-semiconductor-compatible methods. In practice, OS has successfully replaced hydrogenated amorphous Si in high-end liquid crystal display devices and has now become a standard backplane electronic for organic light-emitting diode displays despite the short time since their invention in 2004. For OS to be implemented in next-generation electronics such as back-end-of-line transistor applications in monolithic 3D integration beyond the display applications, however, there is still much room for further study, such as high mobility, immune short-channel effects, low electrical contact properties, etc. This study reviews the brief history of OS and recent progress in device applications from a material science and device physics point of view. Simultaneously, remaining challenges and opportunities in OS for use in next-generation electronics are discussed.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

Mixed-Dimensional Nanoparticle-Nanowire Channels for Flexible Optoelectronic Artificial Synapse with Enhanced Photoelectric Response and Asymmetric Bidirectional Plasticity

A mixed-dimensional dual-channel synaptic transistor composed of inorganic nanoparticles and organic nanowires was fabricated to expand the photoelectric gain range. The device can actualize the sensitization features of the nociceptor and shows improved responsiveness to visible light. Under electrical pulses with different polarities, the apparatus exhibits reconfigurable asymmetric bidirectional plasticity. Moreover, the devices demonstrate good operational tolerance and mechanical stability, retaining more than 60% of their maximum responsiveness after 100 consecutive/bidirectional and 1000 flex/flat operations. The improved photoelectric response of the device endows a high image recognition accuracy of greater than 80%. Asymmetric bidirectional plasticity is used as punishment/reward in a psychological experiment to emulate the improvement of learning motivation and enables real-time forward and backward deflection (+7 and -25°) of artificial muscle. The mixed-dimensional optoelectronic artificial synapses with switchable behavior and electron/hole transport type have important prospects for neuromorphic processing and artificial somatosensory nerves.

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface

A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.

Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material

Currently, for most three-terminal neuromorphic devices, only the gate terminal is active. The inadequate modes and freedom of modulation in such devices greatly hinder the implementation of complex neural behaviors and brain-like thinking strategies in hardware systems. Taking advantage of the unique feature of co-existing in-plane (IP) and out-of-plane (OOP) ferroelectricity in two-dimensional (2D) ferroelectric α-In2Se3, we construct a three-active-terminal neuromorphic device where any terminal can modulate the conductance state. Based on the co-operation mode, controlling food intake as a complex nervous system-level behavior is achieved to carry out positive and negative feedback. Specifically, reinforcement learning as a brain-like thinking strategy is implemented due to the coupling between polarizations in different directions. Compared to the single modulation mode, the chance of the agent successfully obtaining the reward in the Markov decision process is increased from 68% to 82% under the co-operation mode through the coupling effect between IP and OOP ferroelectricity in 2D α-In2Se3 layers. Our work demonstrates the practicability of three-active-terminal neuromorphic devices in handling complex tasks and advances a significant step towards implementing brain-like learning strategies based on neuromorphic devices for dealing with real-world challenges.

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.

Double-Floating-Gate van der Waals Transistor for High-Precision Synaptic Operations

Two-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (∼10⁷), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (Fashion-MNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing.

Photoferroelectric All-van-der-Waals Heterostructure for Multimode Neuromorphic Ferroelectric Transistors

Interface-driven effects in ferroelectric van der Waals (vdW) heterostructures provide fresh opportunities in the search for alternative device architectures toward overcoming the von Neumann bottleneck. However, their implementation is still in its infancy, mostly by electrical control. It is of utmost interest to develop strategies for additional optical and multistate control in the quest for novel neuromorphic architectures. Here, we demonstrate the electrical and optical control of the ferroelectric polarization states of ferroelectric field effect transistors (FeFET). The FeFETs, fully made of ReS₂/hBN/CuInP₂S₆ vdW materials, achieve an on/off ratio exceeding 10⁷, a hysteresis memory window up to 7 V wide, and multiple remanent states with a lifetime exceeding 10³ s. Moreover, the ferroelectric polarization of the CuInP₂S₆ (CIPS) layer can be controlled by photoexciting the vdW heterostructure. We perform wavelength-dependent studies, which allow for identifying two mechanisms at play in the optical control of the polarization: band-to-band photocarrier generation into the 2D semiconductor ReS₂ and photovoltaic voltage into the 2D ferroelectric CIPS. Finally, heterosynaptic plasticity is demonstrated by operating our FeFET in three different synaptic modes: electrically stimulated, optically stimulated, and optically assisted synapse. Key synaptic functionalities are emulated including electrical long-term plasticity, optoelectrical plasticity, optical potentiation, and spike rate-dependent plasticity. The simulated artificial neural networks demonstrate an excellent accuracy level of 91% close to ideal-model synapses. These results provide a fresh background for future research on photoferroelectric vdW systems and put ferroelectric vdW heterostructures on the roadmap for the next neuromorphic computing architectures.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

A multi-timescale synaptic weight based on ferroelectric hafnium zirconium oxide

Brain-inspired computing emerged as a forefront technology to harness the growing amount of data generated in an increasingly connected society. The complex dynamics involving short- and long-term memory are key to the undisputed performance of biological neural networks. Here, we report on sub-µm-sized artificial synaptic weights exploiting a combination of a ferroelectric space charge effect and oxidation state modulation in the oxide channel of a ferroelectric field effect transistor. They lead to a quasi-continuous resistance tuning of the synapse by a factor of 60 and a fine-grained weight update of more than 200 resistance values. We leverage a fast, saturating ferroelectric effect and a slow, ionic drift and diffusion process to engineer a multi-timescale artificial synapse. Our device demonstrates an endurance of more than 10 10 cycles, a ferroelectric retention of more than 10 years, and various types of volatility behavior on distinct timescales, making it well suited for neuromorphic and cognitive computing.

One-Step Preparation of Semiconductor/Dielectric Bilayer Structures for the Simulation of Flexible Bionic Photonic Synapses

Flexible synaptic devices with information sensing, processing, and storage functions are indispensable in the development of wearable artificial intelligence electronic systems. Here, a semiconductor/dielectric bilayer structure was prepared by a one-step deposition method and used for the first time in a flexible biomimetic photonic synaptic transistor device. Specifically, poly(3-hexylthiophene)-block-poly(phenyl isocyanide) with pentafluorophenyl ester (P3HT-b-PPI(5F)) was prepared as the device active layer, where the P3HT segment served as a carrier transport channel and optical gate and the PPI(5F) segment was used for charge trapping. Various biomimetic synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and short-term/long-term memory, were successfully simulated under green light stimulation. An ultra-low energy consumption of 1.82 fJ was achieved with a greatly reduced operating voltage. Further, the "Morse-code" optical decoding was simulated using the excellent synaptic plasticity of the device. In addition, flexible synaptic devices were prepared by a one-step deposition method and can be well-affixed to arbitrary substrates. This has promising applications in the field of wearable bionic electronics.

Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates

In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium-gallium-zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm² at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system.

An artificial remote tactile device with 3D depth-of-field sensation

Flexible tactile neuromorphic devices are becoming important as the impetus for the development of human-machine collaboration. However, accomplishing and further transcending human intelligence with artificial intelligence still confront many barriers. Here, we present a self-powered stretchable three-dimensional remote tactile device (3D-RTD) that performs the depth-of-field (DOF) sensation of external mechanical motions through a conductive-dielectric heterogeneous structure. The device can build a logic relationship precisely between DOF motions of an external active object and sensory potential signals of bipolar sign, frequency, amplitude, etc. The sensory mechanism is revealed on the basis of the electrostatic theory and multiphysics modeling, and the performance is verified via an artificial-biological hybrid system with micro/macroscale interaction. The feasibility of the 3D-RTD as an obstacle-avoidance patch for the blind is systematically demonstrated with a rat. This work paves the way for multimodal neuromorphic device that transcends the function of a biological one toward a new modality for brain-like intelligence.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

Two-Dimensional Perovskite-Gated AlGaN/GaN High-Electron-Mobility-Transistor for Neuromorphic Vision Sensor

The extraordinary optoelectronic properties and continued commercialization of GaN enable it a promising component for neuromorphic visual system (NVS). However, typical GaN-based optoelectronic devices demonstrated to data only show temporary and unidirectional photoresponse in ultraviolet region, which is an insurmountable obstacle for construction of NVS in practical applications. Herein, an ultrasensitive visual sensor with phototransistor architecture consisting of AlGaN/GaN high-electron-mobility-transistor (HEMT) and two-dimensional Ruddlesden-Popper organic-inorganic halide perovskite (2D OIHP) is reported. Utilizing the significant variation in activation energy for ion transport in 2D OIHP (from 1.3 eV under dark to 0.4 eV under illumination), the sensor can efficiently perceive and storage optical information in ultraviolet-visible region. Meanwhile, the photo-enhanced field-effect mechanism in the depletion-mode HEMT enables gate-tunable negative and positive photoresponse, where some typical optoelectronic synaptic functions including inhibitory and excitatory postsynaptic current as well as paired-pulse facilitation are demonstrated. More importantly, a NVS based on the proposed visual sensor array is constructed for achieving neuromorphic visual preprocessing with an improved color image recognition rate of 100%.

A non-linear two-dimensional float gate transistor as a lateral inhibitory synapse for retinal early visual processing

Synaptic transistors that accommodate concurrent signal transmission and learning in a neural network are attracting enormous interest for neuromorphic sensory processing. To remove redundant sensory information while keeping important features, artificial synaptic transistors with non-linear conductance are desired to apply filter processing to sensory inputs. Here, we report the realization of non-linear synapses using a two-dimensional van der Waals (vdW) heterostructure (MoS₂/h-BN/graphene) based float gate memory device, in which the semiconductor channel is tailored via a surface acceptor (ZnPc) for subthreshold operation. In addition to usual synaptic plasticity, the memory device exhibits highly non-linear conductance (rectification ratio >10⁶), allowing bidirectional yet only negative/inhibitory current to pass through. We demonstrate that in a lateral coupling network, such a float gate memory device resembles the key lateral inhibition function of horizontal cells for the formation of an ON-center/OFF-surround receptive field. When combined with synaptic plasticity, the lateral inhibition weights are further tunable to enable adjustable edge enhancement for early visual processing. Our results here hopefully open a new scheme toward early sensory perception via lateral inhibitory synaptic transistors.

Transparent electronic and photoelectric synaptic transistors based on the combination of an InGaZnO channel and a TaOx gate dielectric

A transparent thin film transistor (TFT) based on the combination of an InGaZnO channel and a high-κ (the dielectric constant is about 42.6) TaO_x gate dielectric layer is fabricated. The TFT shows robust anticlockwise hysteresis under DC voltage sweep and synaptic behaviors (i.e., excitatory postsynaptic current, short-term memory plasticity, short-term memory to long-term memory transition, and potentiation and depression) under voltage pulse stimulus. In addition, the TFT shows high responsivity to illumination of light with various wavelengths (ultraviolet and visible light). Synaptic behaviors in response to light pulse stimuli, which could be employed in vision-based neuromorphic applications, are demonstrated. Large conductance change (G_max/G_min > 10) and ultra-low non-linearity (α < 0.5) of the potentiation and depression can be inspired by either gate bias pulses or photoelectric pulses with short pulse widths and small amplitudes.

Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems

The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.

Sputtered Electrolyte-Gated Transistor with Temperature-Modulated Synaptic Plasticity Behaviors

Temperature has always been considered as an essential factor for almost all kinds of semiconductor-based electronic components. In this work, temperature-dependent synaptic plasticity behaviors, which are mimicked by the indium-gallium-zinc oxide thin-film transistors gated with sputtered SiO₂ electrolytes, have been studied. With the temperature increasing from 303 to 323 K, the electrolyte capacitance decreases from 0.42 to 0.11 μF cm^-2. The mobility increases from 1.4 to 3.7 cm² V^-1 s^-1, and the threshold voltage negatively shifts from -0.23 to -0.51 V. Synaptic behaviors under both a single pulse and multiple pulses are employed to study the temperature dependence. With the temperature increasing from 303 to 323 K, the post-synaptic current (PSC) at the resting state increases from 1.8 to 7.3 μA. Under a single gate pulse of 1 V and 1 s, the PSC signal altitude and the PSC retention time decrease from 2.0 to 0.7 μA and 5.1 × 10² to 2.5 ms, respectively. A physical model based on the electric field-induced ion drifting, ionic-electronic coupling, and gradient-coordinated ion diffusion is proposed to understand these temperature-dependent synaptic behaviors. Based on the experimental data on individual transistors, temperature-modulated pattern learning and memorizing behaviors are conceptually demonstrated. The in-depth investigation of the temperature dependence helps pave the way for further electrolyte-gated transistor-based neuromorphic applications.

Fully Printed Optoelectronic Synaptic Transistors Based on Quantum Dot-Metal Oxide Semiconductor Heterojunctions

Optoelectronic synaptic transistors with hybrid heterostructure channels have been extensively developed to construct artificial visual systems, inspired by the human visual system. However, optoelectronic transistors taking full advantages of superior optoelectronic synaptic behaviors, low-cost processes, low-power consumption, and environmental benignity remained a challenge. Herein, we report a fully printed, high-performance optoelectronic synaptic transistor based on hybrid heterostructures of heavy-metal-free InP/ZnSe core/shell quantum dots (QDs) and n-type SnO₂ amorphous oxide semiconductors (AOSs). The elaborately designed heterojunction improves the separation efficiency of photoexcited charges, leading to high photoresponsivity and tunable synaptic weight changes. Under the coordinated modulation of electrical and optical modes, important biological synaptic behaviors, including excitatory postsynaptic current, short/long-term plasticity, and paired-pulse facilitation, were demonstrated with a low power consumption (∼5.6 pJ per event). The InP/ZnSe QD/SnO₂ based artificial vision system illustrated a significantly improved accuracy of 91% in image recognition, compared to that of bare SnO₂ based counterparts (58%). Combining the outstanding synaptic characteristics of both AOS materials and heterojunction structures, this work provides a printable, low-cost, and high-efficiency strategy to achieve advanced optoelectronic synapses for neuromorphic electronics and artificial intelligence.

Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing

A charge trap device based on field-effect transistors (FET) is a promising candidate for artificial synapses because of its high reliability and mature fabrication technology. However, conventional MOSFET-based charge trap synapses require a strong stimulus for synaptic update because of their inefficient hot-carrier injection into the charge trapping layer, consequently causing a slow speed operation and large power consumption. Here, we propose a highly efficient charge trap synapse using III-V materials-based tunnel field-effect transistor (TFET). Our synaptic TFETs present superior subthreshold swing and improved charge trapping ability utilizing both carriers as charge trapping sources: hot holes created by impact ionization in the narrow bandgap InGaAs after being provided from the p⁺-source, and band-to-band tunneling hot electrons (BBHEs) generated at the abrupt p⁺n junctions in the TFETs. Thanks to these advances, our devices achieved outstanding efficiency in synaptic characteristics with a 5750 times faster synaptic update speed and 51 times lower sub-fJ/um² energy consumption per single synaptic update in comparison to the MOSFET-based synapse. An artificial neural network (ANN) simulation also confirmed a high recognition accuracy of handwritten digits up to ∼90% in a multilayer perceptron neural network based on our synaptic devices.

Artificial Vision Adaption Mimicked by an Optoelectrical In2O3 Transistor Array

Simulation of biological visual perception has gained considerable attention. In this paper, an optoelectrical In₂O₃ transistor array with a negative photoconductivity behavior is designed using a side-gate structure and a screen-printed ion-gel as the gate insulator. This paper is the first to observe a negative photoconductivity in electrolyte-gated oxide devices. Furthermore, an artificial visual perception system capable of self-adapting to environmental lightness is mimicked using the proposed device array. The transistor device array shows a self-adaptive behavior of light under different levels of light intensity, successfully demonstrating the visual adaption with an adjustable threshold range to the external environment. This study provides a new way to create an environmentally adaptive artificial visual perception system and has far-reaching significance for the future of neuromorphic electronics.

CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks

Convolutional neural networks (CNNs) have gained much attention because they can provide superior complex image recognition through convolution operations. Convolution processes require repeated multiplication and accumulation operations, which are difficult tasks for conventional computing systems. Compute-in-memory (CIM) that uses parallel data processing is an ideal device structure for convolution operations. CIM based on two-terminal synaptic devices with a crossbar structure has been developed, but unwanted leakage current paths and the high-power consumption remain as the challenges. Here, we demonstrate integrated ferroelectric thin-film transistor (FeTFT) synaptic arrays that can provide efficient parallel programming and data processing for CNNs by the selective and accurate control of polarization in the ferroelectric layer. In addition, three-terminal FeTFTs can act as both nonvolatile memory and access device, which tackle issues from two-terminal devices. An integrated FeTFT synaptic array with parallel programming capabilities can perform convolution operations to extract image features with a high-recognition accuracy.

Artificial Synapses Based on Bovine Milk Biopolymer Electric-Double-Layer Transistors

With the growing demand for bio- and eco-friendly artificial synapses, we propose a novel synaptic transistor using natural bovine-milk-based biocompatible polymers as an electrical double layer (EDL). A method for forming an EDL membrane, which plays a key role in synaptic devices, was established using a milk-based biocompatible polymer. The frequency-dependent capacitance of a milk-based polymer-EDL was evaluated by constructing an EDL capacitor (EDLC) with indium-tin-oxide (ITO) electrode. As a result, a significantly large capacitance (1.48 μF/cm² at 1 Hz) was identified as an EDL effect due to the proton charge of the bovine-milk-based polymer, which is much more superior compared to conventional insulating materials such as SiO₂. Subsequently, by using a milk-based polymer-EDL membrane in the fabrication of electronic synaptic transistors, we successfully implemented important synaptic functions, such as paired-pulse facilitation, dynamic filtering, and synaptic-weight-integration-based logic operations. Therefore, the proposed milk-based biocompatible polymer-EDL membrane offers new opportunities for building eco-friendly and biodegradable artificial synaptic systems.

Deep Ultraviolet Light Stimulated Synaptic Transistors Based on Poly(3-hexylthiophene) Ultrathin Films

Deep ultraviolet (DUV)-light-stimulated artificial synaptic devices exhibit potential applications in various disciplines including intelligent military monitoring, biological and medical analysis, flame detection, etc. Along these lines, we report here a DUV-light-stimulated synaptic transistor fabricated on a poly(3-hexylthiophene) (P3HT) ultrathin film that responds selectively to DUV light. Significantly, our devices have the ability to successfully simulate various synapse-like behaviors including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), short-term memory (STM), long-term memory (LTM), STM-to-LTM transition, and learning and forgetting behaviors. Moreover, the proposed artificial synaptic structures were also fabricated on flexible poly(ethylene terephthalate) (PET) substrates and also successfully simulated typical synaptic behaviors, which could be of great importance for wearable applications.

Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition

We fabricated a nanowire-channel intrinsically stretchable neuromorphic transistor (NISNT) that perceives both tactile and visual information and emulates neuromorphic processing capabilities. The device demonstrated excellent stretching endurance of 1000 stretch cycles while retaining stable electrical properties. The device was then applied as a multisensitive afferent nerve that processes information in parallel. Compatible with skin deformation, the devices are attached to fingers to serve as conformal strain sensors and neuromorphic information-processing units for gesture recognition. The excitatory postsynaptic current in each device represents shape changes and is then analyzed using softmax activation processing of the neural network to recognize gestures. A multistage neural network that uses NISNT was used to further confirm the gestures. This work demonstrated an idea toward multisensory artificial nerves and neuromorphic systems.