All-Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh-density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all-optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light-induced electron trapping and detrapping. It is found that the light-induced multiple memconductance states are nonvolatile. Furthermore, spike-timing-dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.     

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.     

2D Material Based Synaptic Devices for Neuromorphic Computing

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high-performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.     

Bidirectionally Photoresponsive Optoelectronic Transistors with Dual Photogates for All-Optical-Configured Neuromorphic Vision

Bio-inspired neuromorphic vision sensors, integrating optical sensing, and processing functions have attracted significant attention for developing future low-power and high-efficiency imaging systems. However, the compulsory electrical signal modulation to achieve inhibitory behaviors in most reported neuromorphic vision sensors results in additional hardware and computational latency. Herein, bidirectional photoresponsive optoelectronic synapses based on In₂O₃/Al₂O₃/Y6 phototransistors are achieved, realizing all-optical-configured synaptic weight updates enabled by dual photogates. The inhibitory and excitatory photoresponses originate from the photogating effects provided by trapped photogenerated electrons in Al₂O₃ under near-infrared light and the ionized oxygen vacancies in In₂O₃ under ultra-violet light, respectively. The bidirectional phototransistor illustrates outstanding optoelectronic synaptic characteristics with low nonlinearity and asymmetry, demonstrating high efficiencies in both preprocessing and postprocessing tasks, such as noise reduction, contrast enhancement, and pattern recognition. The proposed dual-photogate optoelectronic synapses provide effective strategies to construct high-efficiency neuromorphic vision sensors and in-sensor computing systems.     

Broadband Optoelectronic Synapse Enables Compact Monolithic Neuromorphic Machine Vision for Information Processing

Traditional machine vision is suffering from redundant sensing data, bulky structures, and high energy consumption. Biological-inspired neuromorphic systems are promising for compact and energy-efficient machine vision. Multifunctional optoelectronics enabling multispectrum sensitivity for broadband image sensing, feature extraction, and neuromorphic computing are vital for machine visions. Here, an optoelectronic synapse is designed that enables image sensing, convolutional processing, and computing. Multiple synaptic plasticity triggered by photons can implement photonic computing and information transmission. Convolutional processing is realized by ultralow energy kernel generators fully controlled by photons. Meanwhile, the device shows the ability of conductance modulations under electronic stimulations that implement neuromorphic computing. For the first time, this two-terminal broadband optoelectronic synapse enables front-end retinomorphic image sensing, convolutional processing, and back-end neuromorphic computing. The integrated photonic information encryption, convolutional image preprocessing, and neuromorphic computing capabilities are promising for compact monolithic neuromorphic machine vision systems.     

Recent Developments of Optoelectronic Synaptic Devices Based on Metal Halide Perovskites

Massive data processing with high computing efficiency and low operating power is required owing to the rapid development of artificial intelligence and information technology. However, the von Neumann structure computing system with the separated memory and processor can cause large energy consumption and a low running speed during massive data processing. Therefore, the brain-inspired neuromorphic computing system is developed, that can provide hardware support for emulating biological synaptic functions and realizing highly intensive data processing with low power consumption. As a neuromorphic device, the optoelectronic synaptic device (OSD) is regarded as an ideal device to replace the von Neumann-based computer because of its ultrafast signal transmission, large bandwidth, low energy consumption, and wireless communication. Owing to their unique optoelectronic property, metal halide perovskites (MHPs) have received growing attention as effective photosensitive materials in OSDs. Therefore, the review introduces the recent progress on OSDs based on MHPs (MHPs-OSDs) including the structures and properties of MHPs, and the architectures and performance characteristics of MHPs-OSDs. Furthermore, applications of MHPs-OSDs are presented. Finally, the outlook and opportunity of MHPs-OSDs are discussed.     

Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based arti?cial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS₂ grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS₂ grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning.     

Bio-Inspired Artificial Perceptual Devices for Neuromorphic Computing and Gesture Recognition

Artificial perception technologies capable of sensing and feeling mechanical stimuli like human skins are critical enablers for electronic skins (E-Skins) needed to achieve artificial intelligence. However, most of the reported electronic skin systems lack the capability to process and interpret the sensor data. Herein, a new design of artificial perceptual system integrating ZnO-based synaptic devices with Pt/carbon nanofibers-based strain sensors for stimuli detection and information processing is presented. Benefiting from the controllable ion migration after indium doping, the device can emulate various essential functions, such as short-term/long-term plasticity, paired-pulse facilitation, excitatory post-synaptic current, and synaptic plasticity depending on the number, frequency, amplitude, and width of the applied pulses. The Pt/carbon nanofibers-based strain sensors can detect subtle human motion and convert mechanical stimuli into electrical signals, which are further processed by the ZnO devices. By attaching the integrated devices to finger joints, it is demonstrated that they can recognize handwriting and gestures with a high accuracy. This work offers new insights in designing artificial synapses and sensors to process and recognize information for neuromorphic computing and artificial intelligence applications.     

Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike‐Timing‐Dependent Plasticity

Neuromorphic and cognitive computing with a capability of analyzing complicated information is explored as a new paradigm of intelligent systems. An implementation of a renewable material as an essential building block of an artificial synaptic device is suggested and a flexible and transparent synaptic device based on collagen extracted from fish skin is demonstrated. This device exhibits essential synaptic behaviors including analog memory characteristics, excitatory postsynaptic current, and paired‐pulse facilitation as short‐term plasticity. The brain‐inspired electronic synapse undergoes incremental potentiation and depression when flat or bent. The device emulates spike‐timing‐dependent plasticity when stimulated by engineered pre‐ and post‐neuron spikes with the appropriate time difference between the imposed pulses. The proposed synaptic device has the advantage of being biocompatible owing to use of Mg electrodes and collagen as a naturally abundant protein. This device has a potential to be used in flexible and implantable neuromorphic systems in the future.     

Progress of Si-based Optoelectronic Devices

Si-based optoelectronics is becoming a very active research area due to its potential applications to optical communications. One of the major goals of this study is to realize all-Si optoelectronic integrated circuit. This is due to the fact that Si- based optoelectronic technology can be compatible with Si microelectronic technology. If Si - based optoelectronic devices and integrated circuits can be achieved, it will lead to a new irtformational technological revolution. In the article, the current developments of this exciting field are mainly reviewed in the recent years. The involved contents are the realization of various Si- based optoelectronic devices, such as light- emitting diodes, optical waveguides devices, Si photonic bandgap crystals, and Si laser,etc. Finally, the developed tendency of all-Si optoelectronic integrated technology are predicted in the near future.     

CuInP2S6-Based Electronic/Optoelectronic Synapse for Artificial Visual System Application

With the development of information processing, various neuromorphic synaptic devices have been proposed, including novel devices mimicking multiple sensory systems in the biosome, in which vision is a vital source of information. Due to the pressing issue of high energy consumption and the ever-increasing complexity of practical application scenarios, there is an urgent need to investigate optoelectronic synapses with simple structure but multifunctional capabilities, thereby broadening their application scope. Here, remarkable performances in both electrical and optical operation modes are achieved in multilayer graphene/CuInP₂S₆/Au electronic/optoelectronic device. By modulating the electrical and optical pulses, both short-term and long-term memory can be emulated in the same device, while visual perception, processing, and memorizing functions are demonstrated in this single cell with relatively low energy consumption. In addition, light adaptive behavior can also be simulated through optical–electrical cooperative modulation in the device, further providing a novel and promising strategy for future applications in artificial visual systems.     

超宽带光电子芯片技术研究北大核心

超宽带光电子芯片是下一代无线通信、先进电子信息装备中光纤传输与信号处理的关键元器件,芯片中光子、电子、电磁场之间的相互作用是决定芯片性能的核心因素。文章通过介绍超宽带光电探测器芯片、电光调制器芯片等方面的研究进展,分享课题组在破解上述核心科学问题、提高芯片性能的关键技术方案。     

Hybrid Bacteriorhodopsin/Zinc Oxide Synaptic Photoconductors for Bio-compatible Neuromorphic Devices

Neuromorphic hardware based on artificial synaptic devices has great potential to break the bottleneck of von Neumann architecture, which makes it possible to emulate the working mode of the human brain with low power consumption and high operation efficiency. However, current synaptic devices can barely detect photons and are bio-incompatible for future all-in-one visual perception technology. Here, synaptic photoconductors based on an organic–inorganic hybrid structure, and composed of photosensitive bacteriorhodopsin protein layer and zinc oxide film are reported. The synaptic photoconductors demonstrate tunable synaptic plasticity with the modulation of the light illumination time and power intensity. The working mechanism of the photogating effect induced by the proton pump process of bR protein molecules is further investigated in detail. Assisting with these properties, the imaging memorization and preprocessing function are successfully emulated by the synaptic photoconductors. The prototype photosynaptic devices provide a unique opportunity to realize artificial synapses, enabling neuromorphic hardware.     

All Two-dimensional Integration-Type Optoelectronic Synapse Mimicking Visual Attention Mechanism for Multi-Target Recognition

The human visual attention mechanism enables them to rapidly perceive important information and objects in a complex external scene; this effectively solves the problems of data redundancy, low-resolution images, and substantial computing resources. The process by which the attention system reconstructs the visual information can be considered as integrating internal attention signals with external visual details in the postsynaptic neuron. However, electronic devices that simulate visual attention modulation by incorporating device characteristics into neuromorphic vision systems (NVSs) to achieve visual attention behavior are rarely reported. Herein, a synapse device that integrates optical and electrical stimulation is designed using ReS₂/hBN/monolayer graphene heterojunction to mimic attention regulation and integrate multiple neuron signals successfully. The synapse array can imitate perceptual learning of the human visual system (HVS) to realize visual preprocessing, such as image contrast improvement and weak signal enhancement at the sensory terminal, and overcome data redundancy. Moreover, by applying gate voltage pulses, electric-tunable synaptic plasticity is successfully observed, attributed to the carrier trapping and de-trapping mechanism in the floating layer. Attention stabilization, fluctuation, distraction, and reinforcement are exhibited, simulating the attention behaviors of the HVS. Thus, an NVS with attention mechanism is established depending on the optoelectronic hybrid synaptic plasticity of the device, which successfully mimics the HVS to perform a multi-target recognition task. Furthermore, the effect of device defects on the NVS is rarely evaluated, in which a method is provided to analyze the application results of the NVS when considering uniformity and fault rate. This study may provide new inspiration for developing neuromorphic vision systems for autonomous driving and brainwave control in the future.     

Printed Neuromorphic Devices Based on Printed Carbon Nanotube Thin‐Film Transistors

Hardware implementation of artificial synapse/neuron by electronic/ionic hybrid devices is of great interest for brain‐inspired neuromorphic systems. At the same time, printed electronics have received considerable interest in recent years. Here, printed dual‐gate carbon‐nanotube thin‐film transistors with very high saturation field‐effect mobility (≈269 cm² V^?1 s^–1) are proposed for artificial synapse application. Some important synaptic behaviors including paired‐pulse facilitation (PPF), and signal filtering characteristics are successfully emulated in such printed artificial synapses. The PPF index can be modulated by spike width and spike interval of presynaptic impulse voltages. The results present a printable approach to fabricate artificial synaptic devices for neuromorphic systems.     

Carbon Nanotube Optoelectronic Synapse Transistor Arrays with Ultra-Low Power Consumption for Stretchable Neuromorphic Vision Systems

High-performance stretchable optoelectronic synaptic transistor arrays are key units for constructing and mimicking simulated neuromorphic vision systems. In this study, ultra-low power consumption and low-operation-voltage stretchable all-carbon optoelectronic synaptic thin film transistors (TFTs) using sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) modified with CdSe/ZnS quantum dots as active layers on ionic liquid-based composite elastomer substrates are first reported. The resulting stretchable TFT devices show enhancement-mode characteristics with excellent electrical properties (such as the record on/off ratios up to 10⁵, negligible hysteresis, and small subthreshold swing), excellent mechanical tensile properties (such as the only 12.4% and 6.4% degradations of the carrier mobility after 20% vertical and horizontal strain stretching), and optoelectronic synaptic plasticity (for the recognition of Morse codes) with ultra-low power consumptions (15.38 aJ) at the operating voltage from −1 to 0.2 V. At the same time, the designed nonvolatile conductance of the stretchable SWCNT optoelectronic synapse thin film transistors (SSOSTFTs) stimulated by UV light and the bending angle are first used to simulate stretchable neuromorphic vision systems (including the functions of the crystalline lens and optic cone cells as bionic eyes) for detecting the atmospheric environment with a record accuracy of 95.1% as a bionic eye.     

Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.     

Recent Advances in Transistor‐Based Artificial Synapses

Simulating biological synapses with electronic devices is a re‐emerging field of research. It is widely recognized as the first step in hardware building brain‐like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor‐based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor‐based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor‐based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor‐based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor‐based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor‐based artificial synapses are also presented.     

Emerging MXene-Based Memristors for In-Memory,Neuromorphic Computing,and Logic Operation

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data-intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in-memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high-performance memristors. Herein, the review focuses on the emerging 2D MXene materials-based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene-based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene-based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene-based memristors and more engaging information technology applications.