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.     

Retina-Inspired Artificial Synapses with Ultraviolet to Near-Infrared Broadband Responses for Energy-Efficient Neuromorphic Visual Systems

Neuromorphic visual system with image perception, memory, and preprocessing functions is expected to simulate basic features of the human retina. Organic optoelectronic synaptic transistors emulating biological synapses may be promising candidates for constructing neural morphological visual system. However, the sensing wavelength range of organic optoelectronic synaptic transistors usually limits their potential in artificial multispectral visual perception. Here, retina-inspired optoelectronic synaptic transistors that present broadband responses covering ultraviolet, visible, and near-infrared regions are demonstrated, which leverage the wide-range photoresponsive charge trapping layer and the heterostructure formed between PbS quantum dots and organic semiconductor. Simplified neuromorphic visual arrays are developed to simulate comprehensive image perception, memory, and preprocessing functions. Benefitting from the flexibility of the charge trapping and organic semiconductor layers, a flexible neuromorphic visual array can be fabricated, having an ultralow power consumption of 0.55 fJ per event under a low operating voltage of −0.01 V. More significantly, an accelerating image preprocessing effect can be observed in a wide wavelength range even beyond the perception range of the human visual system, due to the gate-adjustable synaptic plasticity. These devices are highly promising for implementing neuromorphic visual systems with broadband perception, increasing image processing efficiency, and promoting the development of artificial vision.     

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.     

An Array of Light-Stimulated Two-Terminal Synaptic Devices with the Modulation of Electric Polarity

Neuromorphic visual systems based on optoelectronic synaptic devices have been recently studied to simulate the retina and visual cortex of a human being. Now it is shown that an array of optoelectronic synaptic devices based on the two-terminal structure of Si/perovskite/Au may mimic the functionalities of lateral geniculate nucleus (LGN) cells. Benefiting from the photovoltaic effect, the devices can work under a self-powered mode. Diverse synaptic functionalities such as postsynaptic current, paired-pulse facilitation/depression, spike duration-dependent plasticity, spike number-dependent plasticity, and spike rate-dependent plasticity have been simulated. By modulating the electric bias of the devices in the array the simulation of the positional and orientational recognition of the LGN cells is demonstrated.     

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.     

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.     

Nanowires for UV–vis–IR Optoelectronic Synaptic Devices

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non-contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)-based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires-based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs-based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs-based optoelectronic synaptic devices.     

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.     

Hardware Acceleration for Neuromorphic Vision Algorithms

Neuromorphic vision algorithms are biologically inspired models that follow the processing that takes place in the primate visual cortex. Despite their efficiency and robustness, the complexity of these algorithms results in reduced performance when executed on general purpose processors. This paper proposes an application-specific system for accelerating a neuromorphic vision system for object recognition. The system is based on HMAX, a biologically-inspired model of the visual cortex. The neuromorphic accelerators are validated on a multi-FPGA system. Results show that the neuromorphic accelerators are 13.8× (2.6×) more power efficient when compared to CPU (GPU) implementation.     

Bidirectional All‐Optical Synapses Based on a 2D Bi2O2Se/Graphene Hybrid Structure for Multifunctional Optoelectronics

Neuromorphic computing has been extensively studied to mimic the brain functions of perception, learning, and memory because it may overcome the von Neumann bottleneck. Here, with the light‐induced bidirectional photoresponse of the proposed Bi₂O₂Se/graphene hybrid structure, its potential use in next‐generation neuromorphic hardware is examined with three distinct optoelectronic applications. First, a photodetector based on a Bi₂O₂Se/graphene hybrid structure presents positive and negative photoresponsibility of 88 and ?110 A W^?1 achieved by the excitation of visible wavelength and ultraviolet wavelength light at intensities of 1.2 and 0.3 mW cm^?2, respectively. Second, this unique photoresponse contributes to the realization of all optically stimulated long‐term potentiation or long‐term depression to mimic synaptic short‐term plasticity and long‐term plasticity, which are attributed to the combined effect of photoconductivity, bolometric, and photoinduced desorption. Third, the devices are applied to perform digital logic functions, such as “AND” and “OR,” using full light modulation. The proposed Bi₂O₂Se/graphene‐based optoelectronic device represents an innovative and efficient building block for the development of future multifunctional artificial neuromorphic systems.     

基于二维半导体材料光电器件的研究进展

近年来,二维半导体材料因其独特的晶体结构和优良的电子、光电特性吸引了众多科研人员的关注。利用这些材料作为有源沟道,制备出了许多新颖的器件结构,性能较传统器件有很大的提升。在各种器件应用中,基于二维材料的光电探测器由于能够实现红外及太赫兹波段的光探测,得到了最为广泛的研究。综述了近年来二维材料在光电器件领域的应用,介绍了光电探测器的主要参数,从电极制备、异质结构筑、量子点和分子掺杂、表面等离激元耦合以及界面屏蔽5方面介绍了目前在二维材料中调控光电性能的方法,对已有方法进行了总结,并且对未来的发展进行了讨论。     

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.     

Linear Classification Function Emulated by Pectin-Based Polysaccharide-Gated Multiterminal Neuron Transistors

Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi-input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi-terminal pectin-gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in-plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.     

Short-Wave Infrared Synaptic Phototransistor with Ambient Light Adaptability for Flexible Artificial Night Visual System

Flexible organic monitoring system that can work in short-wave infrared (SIR) region has great potential in autonomous driving, night driving safety, military encryption, biomedical imaging, and robot engineering. Especially, the development of infrared artificial vision system device that can autonomously improve the computing speed and adapt to different brightness ambient light is very important. However, it is a challenge for mimicking infrared visual adaptation because of the need for infrared absorbing materials and the need to control the concentration of carriers. In this study, inorganic quantum dot material is combined with organic materials to promote carrier separation and introduce interface defects that adjust the carrier concentration in transistors, which induce the synaptic behavior under SIR (1100 nm) light in darkness and the ability to adapt to white ambient light. Furthermore, the device array realizes the image recognition of SIR light at night with white ambient light of different brightness, exhibiting good self-adaptability, and strong anti-interference ability. These results demonstrate promising applications of the infrared synaptic phototransistors in adapted bionic optoelectronic devices.     

二维半导体光电性质与器件研究进展简述

二维半导体材料,如过渡金属硫族化合物,以其在光电器件方面展现出的独特性能与巨大潜力,成为后摩尔时代有极大发展前景的新半导体材料.二维材料具有独特的光电性质,如直接带隙的电子结构,谷自旋电子学特性,强激子效应等,而利用以上性质,此类材料可用于光探测器、场效应晶体管、高效微纳传感器、光电子电路等微纳光电器件中.因此,以过渡金属硫族化合物为代表的二维半导体材料无论在基础科学与未来应用方面,都是重要的备选材料.     

Towards engineering in memristors for emerging memory and neuromorphic computing:A review

Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.     

Tuning Memristivity by Varying the Oxygen Content in a Mixed Ionic–Electronic Conductor

The rising interest shown for adaptable electronics and brain‐inspired neuromorphic hardware increases the need for new device architectures and functional materials to build such devices. The rational design of these memory components also benefits the comprehension and thus the control over the microscopic mechanisms at the origin of memristivity. In oxide‐based valence‐change memories, the control of the oxygen drift and diffusion kinetics is a key aspect in obtaining the gradual analog‐type change in resistance required for artificial synapse applications. However, only a few devices are designed with this in mind, as they are commonly built around ionic insulating active materials. This shortcoming is addressed by using a mixed ionic–electronic conductor as functional memristive material. This work demonstrates how the oxygen content in La₂NiO_4+δ (L2NO4), tuned through post‐annealing treatments, has a critical influence on the memory characteristics of L2NO4‐based memristive devices. The presence of interstitial oxygen point defects in L2NO4 affects both its structure and electrical properties. High oxygen stoichiometry in the pristine state leads to an increased electrical conductivity, ultimately resulting in an improved memory window with highly multilevel, analog‐type memory programing capabilities, desirable for analog computing and synaptic applications in particular.     

Boolean Logic Computing Based on Neuromorphic Transistor

General-purpose computers usually use logic gate computing units based on complementary metal oxide semiconductors (CMOS). Due to the separate memory and computing units in Von Neumann architecture, data transmission requires great energy and time consumption. Developing novel neuromorphic devices and comprehensively investigating their logical computing mode are crucial to achieve high-performance and low-power neuromorphic computation. Here, a systematic summary of Boolean logic computing based on emerging neuromorphic transistors is presented. This summary encompasses logical operation modes, materials, device structures, and working mechanisms. The input mode of Boolean logic operation is classified into electrical input, optical input, and synergistic optical/electrical input. Besides, additional modulation strategies to construct programmable logic functions by electrical, optical, and thermal signals are also summarized. These strategies hold great significance as they enable dynamic reconfiguration of logic operations and provide neuromorphic devices with decision-making capabilities. Finally, the application prospects and current challenges to Boolean logic computing based on dendritic integration are discussed from the aspects of device integration, synergistic input/modulation modes, auxiliary peripheral circuit, software/hardware system, etc. It is believed that comprehensive investigations on neuromorphic Boolean logic operations are crucial to push forward the development of future neuromorphic computing toward high efficiency and high integration density.     

Biocompatible and Biodegradable Neuromorphic Device Based on Hyaluronic Acid for Implantable Bioelectronics

Neurodegenerative disorders are challenging issues for initial diagnosis and cure. False signals from neurons that make up the brain must be corrected. To treat neurodegenerative diseases, neuromorphic devices are inserted into the body and connected to nerves. However, there are major concerns regarding implanting these devices into living bodies, that is, toxicity caused by the materials and the need for an additional operation to remove the device after treatment. In this research, a neuromorphic device is fabricated based on hyaluronic acid (HA), which is biocompatible and biodegradable, that meets the requirements for implantable bioelectronics. The fabricated device have a paired-pulse facilitation index of ≈121.00% and short term-to-long term memory transition behavior that resembled human learning-experience behavior. It is confirmed that the synaptic behavior mechanism of the device is due to an Mg oxide layer formed at the Mg/HA interface. Biodegradability and cell cytotoxicity tests confirmed the suitability of HA-based neuromorphic devices as implantable bioelectronics. Based on the results, it is believed that such implantable devices will lead to better healthcare.     

Flexible and Stretchable Smart Display: Materials,Fabrication, Device Design,and System Integration

Recent technological advances in nanomaterials have driven the development of high‐performance light‐emitting devices with flexible and stretchable form factors. Deformability in such devices is mainly achieved by replacing the rigid materials in the device components with flexible nanomaterials and their assemblies (e.g., carbon nanotubes, silver nanowires, graphene, and quantum dots) or with intrinsically soft materials and their composites (e.g., polymers and elastomers). Downscaling the dimensions of the functional materials to the nanometer range dramatically decreases their flexural rigidity, and production of polymer/elastomer composites with functional nanomaterials provides light‐emitting devices with flexibility and stretchability. Furthermore, monolithic integration of these light‐emitting devices with deformable sensors furnishes the resulting display with various smart functions such as force/capacitive touch‐based data input, personalized health monitoring, and interactive human–machine interfacing. These ultrathin, lightweight, and deformable smart optoelectronic devices have attracted widespread interest from materials scientists and device engineers. Here, a comprehensive review of recent progress concerning these flexible and stretchable smart displays is presented with a focus on materials development, fabrication techniques, and device designs. Brief overviews of an integrated system of advanced smart displays and cutting‐edge wearable sensors are also presented, and, to conclude, a discussion of the future research outlook is given.