Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti3C2Tx MXenes Floating Gate

The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.     

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.     

Transparent and Flexible Inorganic Perovskite Photonic Artificial Synapses with Dual-Mode Operation

With the rapid development of artificial intelligence, the simulation of the human brain for neuromorphic computing has demonstrated unprecedented progress. Photonic artificial synapses are strongly desirable owing to their higher neuron selectivity, lower crosstalk, wavelength multiplexing capabilities, and low operating power compared to their electric counterparts. This study demonstrates a highly transparent and flexible artificial synapse with a two-terminal architecture that emulates photonic synaptic functionalities. This optically triggered artificial synapse exhibits clear synaptic characteristics such as paired-pulse facilitation, short/long-term memory, and synaptic behavior analogous to that of the iris in the human eye. Ultraviolet light illumination-induced neuromorphic characteristics exhibited by the synapse are attributed to carrier trapping and detrapping in the SnO₂ nanoparticles and CsPbCl₃ perovskite interface. Moreover, the ability to detect deep red light without changes in synaptic behavior indicates the potential for dual-mode operation. This study establishes a novel two-terminal architecture for highly transparent and flexible photonic artificial synapse that can help facilitate higher integration density of transparent 3D stacking memristors, and make it possible to approach optical learning, memory, computing, and visual recognition.     

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.     

Electrolyte-gated transistors for neuromorphic applications

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck.To enhance computing performance,neuromorphic computing systems that can simulate the function of the human brain are being developed.Artificial synapses are essential electronic devices for neuromorphic architectures,which have the ability to perform signal processing and storage between neighboring artificial neurons.In recent years,electrolyte-gated transistors(EGTs)have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications.Among the various electronic devices,EGT-based artificial synapses offer the benefits of good stability,ultra-high linearity and repeated cyclic symmetry,and can be constructed from a variety of materials.They also spatially separate“read”and“write”operations.In this article,we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications.We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses.Then,we review different types of channels and electrolyte materials for EGT-based artificial synapses.Finally,we review the potential applications in biological functions.     

A Reliable All-2D Materials Artificial Synapse for High Energy-Efficient Neuromorphic Computing

High-performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy-efficient neuromorphic computing. Here, a two-terminal floating-gate memory (2TFGM) based artificial synapse built from all-2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all-2D 2TFGM for high-speed and low-power neuromorphic computing.     

Photoelectric Synaptic Plasticity Realized by 2D Perovskite

Recently, several light‐stimulated artificial synaptic devices have been proposed to mimic photonic synaptic plasticity for neuromorphic computing. Here, the photoelectric synaptic plasticity based on 2D lead‐free perovskite ((PEA)₂SnI₄) is demonstrated. The devices show a photocurrent activation in response to a light stimulus in a neuron‐like way and exhibit several essential synaptic functions such as short‐term plasticity (STP) and long‐term plasticity (LTP) as well as their transmission based on spike frequency control. The strength of synaptic connectivity can be effectively modulated by the duration, irradiance, and wavelength of light spikes. The ternary structure of (PEA)₂SnI₄ causes it to possess varied photoelectric properties by composition control, which enhances the complexity and freedoms required by neuromorphic computing. The physical mechanisms of the memory effect are attributed to two distinct lifetimes of photogenerated carrier trapping/detrapping processes modulated by controlling the proportion of Sn vacancies. This work demonstrates the great potential of (PEA)₂SnI₄ as a platform to develop future multifunctional artificial neuromorphic systems.     

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.     

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.     

Redox MXene Artificial Synapse with Bidirectional Plasticity and Hypersensitive Responsibility

Artificial synapses are key elements for the nervous system which is an emulation of sensory and motor neuron signal transmission. Here, the design and fabrication of redox-behavior the metal carbide nanosheets, termed MXene artificial synapse, which uses a highly-conductive MXene electrode, are reported. Benefiting from the special working mechanism of ion migration with adsorption and insertion, the device achieves world-record power consumption (460 fW) of two-terminal synaptic devices, and so far, the bidirectionally functioned synaptic device could effectively respond to ultra-small stimuli at an amplitude of ±80 mV, even exceeding that of a biological synapse. Potential applications have also been demonstrated, such as dendritic integration and memory enhancement. The special strategy and superior electrical characteristics of the bidirectionally functioned electronic device pave the way to high-power-efficiency brain-inspired electronics and artificial peripheral systems.     

Tailoring Neuroplasticity in a Ferroelectric-Gated Multi-Terminal Synaptic Transistor by Bi-Directional Modulation for Improved Pattern Edge Recognition

The dynamic modulation of the plasticity of artificial neuromorphic devices facilitates a wide range of neuromorphic functions. However, integrating diverse plasticity modulation techniques into a single device presents a challenge due to limitations in the device structure design. Here, a multiterminal artificial synaptic device capable of bi-directional modulation on its plasticity is proposed. Significantly, the conversion of inhibitory and excitatory synaptic plasticity can be achieved not only by modifying the polarity of the presynaptic voltage spike but also by exchanging its input terminal between top and bottom gate while maintaining the same presynaptic stimuli. This unique bi-directional modulation of synaptic plasticity has been attributed to two distinct physical mechanisms: nonvolatile ferroelectric polarization and interface charge trap-induced memory characteristics. Additionally, the effective dynamic modulation of the synaptic behaviors is quantified under different back-gate bias and verified in the constructed neural network perceptron. Further, a visual simulation demonstrates the enhanced clarity and precision of edge recognition through the back-gate modulation in the artificial synapses. This study provides a strategy to fulfill diversified modulation on synaptic plasticity in ferroelectric-gated transistors, thereby prompting efficient and controllable neuromorphic visual systems.     

Photoactive Electro-Controlled Visual Perception Memory for Emulating Synaptic Metaplasticity and Hebbian Learning

In bionic technology, it has become an innovative process imitating the functionality and structuralism of human biological systems to exploit advanced artificial intelligent machines. Bionics plays a significant role in environmental protection, especially for its low energy loss. By fusing the concept of receptor-like sensing component and synapse-like memory, the photoactive electro-controlled optical sensory memory (PE-SM) is proposed and realized in a single device, which endows a simple methodology of reducing power consumption by photoactive electro-control. The PE-SM is the system built with the stacked atomically thick materials, in which rhenium diselenide serves as a robust photosensor, hexagonal boron nitride serves as a tunneling dielectric, and graphene serves as a charge-storage layer. With the features of the PE-SM, it performs synaptic metaplasticities under optical spikes. In addition, a simulated spiking neural network composed of 24 × 24 PE-SMs is further presented in an unsupervised machine learning environment, performing image recognition via the Hebbian rule. The PE-SM not only improves the neuromorphic computing efficiency but also simplifies the circuit-size structure. Eventually, the concept of photoactive electro-control can extend to other photosensitive 2D materials and provide a new approach of constructing either visual perception memory or photonic synaptic devices.     

Mimicking Neuroplasticity in a Hybrid Biopolymer Transistor by Dual Modes Modulation

Neuromorphic computing systems that are capable of parallel information storage and processing with high area and energy efficiencies, offer important opportunities for future storage systems and in‐memory computing. Here, it is shown that a carbon dots/silk protein (CDs/silk) blend can be used as a light‐tunable charge trapping medium to fabricate an electro‐photoactive transistor synapse. The synaptic device can be optically operated in volatile or nonvolatile modes, ensuring concomitant short‐term and long‐term neuroplasticity. The synaptic‐like behaviors are attributed to the photogating effect induced by trapped photogenerated electrons in the hybrid CDs/silk film which is confirmed with atomic force microscopy based electrical techniques. In addition, system‐level pattern recognition capability of the synaptic device is evaluated by a single‐layer perceptron model. The remote optical operation of neuromorphic architecture provides promising building blocks to complete bioinspired photonic computing paradigms.     

硫系非晶态半导体人工神经突触的可塑性

模拟大脑中的神经突触是实现下一代计算机——类脑神经形态计算的关键一步。为了利用光子模拟神经突触的可塑性进而发展全光人工神经突触器件,文章开展了基于可控光诱导抑制效应的硫系非晶态半导体人工神经突触的实验研究。分析了材料化学组分和抽运光功率对该人工神经突触的调控作用,描述了该人工神经突触的可塑性。结果表明掺入不同杂质的硫系非晶态半导体平面波导具有不同的可控光诱导抑制过程,且抑制深度受控于抽运光功率的变化。基于这些特性,该人工神经突触展现出了配对脉冲易化功能、短程抑制功能、长程抑制功能,具有良好的可塑性。     

Piezotronic Graphene Artificial Sensory Synapse

The human somatosensory system, consisting of receptors, transmitters, and synapses, functions as the medium for external mechanical stimuli perception and sensing signal delivery/processing. Developing sophisticated artificial sensory synapses with a high performance, uncomplicated fabrication process, and low power consumption is still a great challenge. Here, a piezotronic graphene artificial sensory synapse developed by integrating piezoelectric nanogenerator (PENG) with an ion gel–gated transistor is demonstrated. The piezopotential originating from PENG can efficiently power the synaptic device due to the formation of electrical double layers at the interface of the ion gel/electrode and ion gel/graphene. Meanwhile, the piezopotential coupling is capable of linking the spatiotemporal strain information (strain amplitude and duration) with the postsynaptic current. The synaptic weights can be readily modulated by the strain pulses. Typical properties of a synapse including excitation/inhibition, synaptic plasticity, and paired pulse facilitation are successfully demonstrated. The dynamic modulation of a sensory synapse is also achieved based on dual perceptual presynaptic PENGs coupling to a single postsynaptic transistor. This work provides a new insight into developing piezotronic synaptic devices in neuromorphic computing, which is of great significance in future self‐powered electronic skin with artificial intelligence, a neuromorphic interface for neurorobotics, human–robot interaction, an intelligent piezotronic transistor, etc.     

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.     

Sub-Femtojoule-Energy-Consumption Conformable Synaptic Transistors Based on Organic Single-Crystalline Nanoribbons

Inspired from powerful functionalities of human brain, artificial synapses are innovated continuously for the construction of brain-like neuromorphic electronics. The quest to rival the ultralow energy consumption of biological synapses has become highly compelling, but remains extremely difficult due to the lack of appropriate materials and device construction. In this study, organic single-crystalline nanoribbon active layer and elastic embedded photolithographic electrodes are first designed in synaptic transistors to reduce energy consumption of single device. The minimum energy consumption (0.29 fJ) of one synaptic event is far lower than that of biological synapse (10 fJ). Notably, sub-femtojoule-energy-consumption synaptic transistors can simulate various biological plastic behaviors even under different tensile and compressive strains, offering a new guidance for the construction of ultralow-energy-consuming neuromorphic electronic devices and the development of flexible artificial intelligence electronics in the future.     

Artificial Synapse Based on Oxygen Vacancy Migration in Ferroelectric-Like C-Axis-Aligned Crystalline InGaSnO Semiconductor Thin-Film Transistors for Highly Integrated Neuromorphic Electronics

Artificial synapses are a key component of neuromorphic computing systems. To achieve high-performance neuromorphic computing ability, a huge number of artificial synapses should be integrated because the human brain has a huge number of synapses (≈10¹⁵). In this study, a coplanar synaptic, thin-film transistor (TFT) made of c-axis-aligned crystalline indium gallium tin oxide (CAAC–IGTO) is developed. The electrical characteristics of the biological synapses such as inhibitory postsynaptic current (IPSC), paired-pulse depression (PPD), short-term plasticity (STP), and long-term plasticity at V_DS = 0.1 V, are demonstrated. The measured synaptic behavior can be explained by the migration of positively charged oxygen vacancies (V_o⁺/V_o⁺⁺) in the CAAC–IGTO layer. The mechanism of implementing synaptic behavior is completely new, compared to previous reports using electrolytes or ferroelectric gate insulators. The advantage of this device is to use conventional gate insulators such as SiO₂ for synaptic behavior. Previous studies use chitosan, Ta₂O₃, SiO₂ nanoparticles , Gd₂O₃, and HfZrO_x for gate insulators, which cannot be used for high integration of synaptic devices. The metal–oxide TFTs, widely used in the display industry, can be applied to the synaptic transistors. Therefore, CAAC–IGTO synaptic TFT can be a good candidate for application as an artificial synapse for highly integrated neuromorphic chips.     

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.     

Reconfigurable Logic‐in‐Memory and Multilingual Artificial Synapses Based on 2D Heterostructures

Nonvolatile logic devices have attracted intensive research attentions recently for energy efficiency computing, where data computing and storage can be realized in the same device structure. Various approaches have been adopted to build such devices; however, the functionality and versatility are still very limited. Here, 2D van der Waals heterostructures based on direct bandgap materials black phosphorus and rhenium disulfide for the nonvolatile ternary logic operations is demonstrated for the first time with the ultrathin oxide layer from the black phosphorus serving as the charge trapping as well as band‐to‐band tunneling layer. Furthermore, an artificial electronic synapse based on this heterostructure is demonstrated to mimic trilingual synaptic response by changing the input base voltage. Besides, artificial neural network simulation based on the electronic synaptic arrays using the handwritten digits data sets demonstrates a high recognition accuracy of 91.3%. This work provides a path toward realizing multifunctional nonvolatile logic‐in‐memory applications based on novel 2D heterostructures.