Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for art...Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for artificial intelligence sensing.For instance,the incorporation of low-dimensional materials(e.g.,quantum dots,carbon nanotubes,and two-dimensional materials)optimizes device optoelectronic properties,while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor(CMOS)compatibility.Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption,offering a new paradigm for highly integrated,energy-efficient real-time perception.However,critical challenges—including device non-uniformity caused by material interface defects,system instability induced by memristor conductance drift,and environmental adaptability under complex illumination—remain barriers to scalable applications.This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure,operational mechanisms,materials,and applications.It explores the pivotal roles of memristors,electrolyte-gated transistors,and other neuromorphic devices in optical signal perception and information processing,with a focus on their implementations in visual perception tasks and future prospects.展开更多
As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and el...As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.展开更多
The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonm...The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.展开更多
The increasing complexity of intelligent sensing environments,driven by the growth of Internet of Things technologies,has created a strong demand for neuromorphic systems capable of real-time,low-power multisensory pe...The increasing complexity of intelligent sensing environments,driven by the growth of Internet of Things technologies,has created a strong demand for neuromorphic systems capable of real-time,low-power multisensory perception.Traditional sensory architectures,constrained by single-modal processing and centralized computing,struggle to meet the requirements of diverse and dynamic input conditions.Multisensory neuromorphic devices offer a promising solution by mimicking the distributed,event-driven processing of biological systems.Recent efforts have explored synaptic devices and material systems that respond to various input modalities,including visual,tactile,thermal,and chemical stimuli.However,challenges remain in signal conversion,encoding compatibility,and the fusion of heterogeneous inputs without loss of unisensory information.This review provides a comprehensive overview of the physical mechanisms,device behaviors,and integration strategies that underpin signal processing in neuromorphic hardware.We highlight synaptic mechanisms conducive to cross-modal interaction,analyze representative signal fusion approaches at the device level,and discuss future directions for constructing efficient,scalable,and biologically inspired multisensory neuromorphic systems.展开更多
High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic f...High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.展开更多
The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,fle...The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.展开更多
All-optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption,meeting the application demands of the current artificial int...All-optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption,meeting the application demands of the current artificial intelligence era.However,developing all-optically controlled devices that combine high performance and high reproducibility remains a significant challenge.In this work,we demonstrate an all-optically controlled artificial synapse based on ZnO and Cs_(2)CoCl_(4)single crystal connected structure,which integrates light information sensing and processing capabilities.Relying on the simple series-connected structure,as well as the positive photoconductance of ZnO and the negative photoconductance of Cs_(2)CoCl_(4),the optically controlled bidirectional synaptic plasticity is realized under ultraviolet and visible light stimulation without additional voltage modulation in the all-optically controlled synapse.In addition,leveraging its ultraviolet-enhanced feature extraction and visible-suppression capabilities,the all-optically controlled synapse can act as denoising units in bioinformation preprocessing and weight-updating units in feature recognition.The proposed all-optically controlled synapses exhibit excellent information perception,low-level noise reduction,and high-level cognition functions for bioinformation recognition under complex light conditions.We believe that this work can provide structural-level insights and inspirations in the design and fabrication of all-optically controlled synapses to promote the application for efficient neuromorphic vision in the future.展开更多
Neuromorphic cameras,or dynamic vision sensors,are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level.Recently,they garnered increasing interest...Neuromorphic cameras,or dynamic vision sensors,are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level.Recently,they garnered increasing interest due to their extremely high temporal resolution,wide dynamic range,low power consumption,and high pixel bandwidth.Despite their advantages,most existing three-dimensional (3D) event imaging solutions rely on multicamera configurations,which are costly,complex,and challenging to synchronize.In this study,we introduce a new framework for four-dimensional (4D) event imaging using a single static neuromorphic camera.We take advantage of the inherent sparsity of event data to combine optically encoded stereo channels into a single event camera.By utilizing optical channel multiplexing,we maintain sensor resolution while retaining the key advantages of event cameras.展开更多
Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulatio...Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulation toward dynamic,network-oriented plasticity design,offering enhanced computational accuracy and functional relevance.This review highlights how diversified plasticity behaviors,including multilevel long-term potentiation and depression for spatial models,tunable short-term memory for temporal models,as well as wavelength-selective response,excitatory and inhibitory synergy,and adaptive threshold modulation,collectively support key tasks such as stable learning,temporal processing,and context-aware adaptation.Beyond behavioral innovations,strategies such as multifunctional single-device integration,multimodal fusion,and heterogeneous system assembly enable compact,energy-efficient,and versatile neuromorphic architectures.Recent developments at the array level further demonstrate high-performance scalability and system-level applicability.Despite notable progress,current modulation strategies remain constrained in flexibility,diversity,and large-scale coordination.Future research should focus on enriching the behavioral repertoire of plasticity,advancing crossmodal convergence,and improving array-level uniformity,paving the way toward deployable,high-efficiency neuromorphic intelligence.展开更多
Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural n...Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.展开更多
In recent years,research focusing on synaptic device based on phototransistors has provided a new method for asso-ciative learning and neuromorphic computing.A TiO_(2)/AlGaN/GaN heterostructure-based synaptic phototra...In recent years,research focusing on synaptic device based on phototransistors has provided a new method for asso-ciative learning and neuromorphic computing.A TiO_(2)/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured,integrating a TiO_(2)nanolayer gate and a two-dimensional electron gas(2DEG)channel to mimic the synaptic weight and the synaptic cleft,respectively.The maximum drain to source current is 10 nA,while the device is driven at a reverse bias not exceeding-2.5 V.A excitatory postsynaptic current(EPSC)of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35μW·cm^(-2).Multiple synaptic neuromorphic functions,including EPSC,short-term/long-term plasticity(STP/LTP)and paried-pulse facilitation(PPF),are effectively mimicked by our GaN-based het-erostructure synaptic device.In the typical Pavlov’s dog experiment,we demonstrate that the device can achieve"retraining"process to extend memory time through enhancing the intensity of synaptic weight,which is similar to the working mecha-nism of human brain.展开更多
The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorph...The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.展开更多
To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the vario...To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.展开更多
The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromo...The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromorphic comput-ing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence.Among various neuromorphic devices,the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consump-tion,multimodal sensing/recording capabilities,and multifunctional integration.Moreover,the emerging optoelectronic neuro-morphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neu-romorphic computing field.Therefore,this article reviews recent advancements in electrolyte-gated optoelectronic neuromor-phic transistors.First,it provides an overview of artificial optoelectronic synapses and neurons,discussing aspects such as device structures,operating mechanisms,and neuromorphic functionalities.Next,the potential applications of optoelectronic synapses in different areas such as artificial visual system,pain system,and tactile perception systems are elaborated.Finally,the current challenges are summarized,and future directions for their developments are proposed.展开更多
Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption.MXene-Ti_(3)C_(2)T_(x),an emerging two...Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption.MXene-Ti_(3)C_(2)T_(x),an emerging twodimensional material,stands out as an ideal candidate for fabricating neuromorphic devices.Its exceptional electrical performance and robust mechanical properties make it an ideal choice for this purpose.This review aims to uncover the advantages and properties of MXene-Ti_(3)C_(2)T_(x)in neuromorphic devices and to promote its further development.Firstly,we categorize several core physical mechanisms present in MXene-Ti_(3)C_(2)T_(x)neuromorphic devices and summarize in detail the reasons for their formation.Then,this work systematically summarizes and classifies advanced techniques for the three main optimization pathways of MXene-Ti_(3)C_(2)T_(x),such as doping engineering,interface engineering,and structural engineering.Significantly,this work highlights innovative applications of MXene-Ti_(3)C_(2)T_(x)neuromorphic devices in cutting-edge computing paradigms,particularly near-sensor computing and in-sensor computing.Finally,this review carefully compiles a table that integrates almost all research results involving MXene-Ti_(3)C_(2)T_(x)neuromorphic devices and discusses the challenges,development prospects,and feasibility of MXene-Ti_(3)C_(2)T_(x)-based neuromorphic devices in practical applications,aiming to lay a solid theoretical foundation and provide technical support for further exploration and application of MXene-Ti_(3)C_(2)T_(x)in the field of neuromorphic devices.展开更多
As an emerging memory device,memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption.This review paper focuses on the application of low-power-based memrist...As an emerging memory device,memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption.This review paper focuses on the application of low-power-based memristors in various aspects.The concept and structure of memristor devices are introduced.The selection of functional materials for low-power memristors is discussed,including ion transport materials,phase change materials,magnetoresistive materials,and ferroelectric materials.Two common types of memristor arrays,1T1R and 1S1R crossbar arrays are introduced,and physical diagrams of edge computing memristor chips are discussed in detail.Potential applications of low-power memristors in advanced multi-value storage,digital logic gates,and analogue neuromorphic computing are summarized.Furthermore,the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.展开更多
As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain&...As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain's neural networks.Volatile memristors,particularly Mott and diffusive memristors,have garnered significant attention for their ability to emulate neuronal dynamics,such as spiking and firing patterns,enabling the development of reconfigurable and adaptive computing systems.Recent advancements include the implementation of leaky integrate-and-fire neurons,Hodgkin-Huxley neurons,opto-electronic neurons,and time-surface neurons,all utilizing volatile memristors to achieve efficient,low-power,and highly inte-grated neuromorphic systems.This paper reviews the latest progress in volatile memristor-based artificial neurons,highlight-ing their potential for energy-efficient computing and integration with artificial synapses.We conclude by addressing chal-lenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic com-puting.展开更多
Neuromorphic devices,inspired by the intricate architecture of the human brain,have garnered recognition for their prodigious computational speed and sophisticated parallel computing capabilities.Vision,the primary mo...Neuromorphic devices,inspired by the intricate architecture of the human brain,have garnered recognition for their prodigious computational speed and sophisticated parallel computing capabilities.Vision,the primary mode of external information acquisition in living organisms,has garnered substantial scholarly interest.Notwithstanding numerous studies simulating the retina through optical synapses,their applications remain circumscribed to single-mode perception.Moreover,the pivotal role of temperature,a fundamental regulator of biological activities,has regrettably been relegated to the periphery.To address these limitations,we proffer a neuromorphic device endowed with multimodal perception,grounded in the principles of light-modulated semiconductors.This device seamlessly accomplishes dynamic hybrid visual and thermal multimodal perception,featuring temperature-dependent paired pulse facilitation properties and adaptive storage.Crucially,our meticulous examination of transfer curves,capacitance–voltage(C–V)tests,and noise measurements provides insights into interface and bulk defects,elucidating the physical mechanisms underlying adaptive storage and other functionalities.Additionally,the device demonstrates a variety of synaptic functionalities,including filtering properties,Ebbinghaus curves,and memory applications in image recognition.Surprisingly,the digital recognition rate achieves a remarkable value of 98.8%.展开更多
Artificial multisensory devices play a key role in human-computer interaction in the field of artificial intelligence(AI).In this work,we have designed and constructed a novel olfactory-visual bimodal neuromorphic car...Artificial multisensory devices play a key role in human-computer interaction in the field of artificial intelligence(AI).In this work,we have designed and constructed a novel olfactory-visual bimodal neuromorphic carbon nanotube thin film transistor(TFT)arrays for artificial olfactory-visual multisensory synergy recognition with a very low power consumption of 25 aJ for a single pulse,employing semiconducting single-walled carbon nanotubes(sc-SWCNTs)as channel materials and gas sensitive materials,and poly[[4,8-bis[5-(2-ethylhexyl)-2-thienyl]benzo[1,2-b:4,5-b0]dithiophene-2,6-diyl]-2,5-thiophenediyl-[5,7-bis(2-ethylhexyl)-4,8-dioxo-4H,8H-benzo[1,2-c:4,5-c0]dithio-phene-1,3-diyl]](PBDB-T)as the photosensitive material.It is noted that it is the first time to realize the simulation of olfactory and visual senses(from 280 nm to 650 nm)with the wide operating temperature range(0-150℃)in a single SWCNT TFT device and successfully simulate the recovery of olfactory senses after COVID-19 by olfactory-visual synergy.Furthermore,our SWCNT neuromorphic TFT devices with a high IOn/IOff ratio(up to 10^(6))at a low operating voltage(−2 to 0.5 V)can mimic not only the basic biological synaptic functions of olfaction and vision(such as paired-pulse facilitation,short-term plasticity,and long-term plasticity),but also optical wireless communication by Morse code.The proposed multisensory,broadband light-responsive,low-power synaptic devices provide great potential for developing AI robots to face complex external environments.展开更多
Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of sh...Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of short-term and long-term synaptic plasticity behaviors of the biological synapses is an es-sential step.However,the electronic synaptic devices suffer from higher variability issues which hinder the application of such devices to build neuromorphic systems.For practical applications,it is essen-tial to minimize the cycle-to-cycle and device-to-device variations in the synaptic functions of artifi-cial electronic synapses.This study involves the fabrication of diffusive memristor devices using WTe_(2) chalcogenide as the main switching material.The choice of the switching material provides a facile so-lution to the variability problem.The greater uniformity in the switching characteristics of the WTe_(2)-based memristor offers higher uniformity for the synaptic emulation.These devices exhibit both volatile and nonvolatile switching properties,allowing them to emulate both short-term and long-term synaptic functions.The WTe_(2)-based electronic synaptic devices present a high degree of uniformity for the emula-tion of various essential biological synaptic functions including short-term potentiation(STP),long-term potentiation(LTP),long-term depression(LTD),spike-rate-dependent plasticity(SRDP),and spike-timing-dependent plasticity(STDP).A higher recognition accuracy of∼92%is attained for pattern recognition using the modified National Institute of Standards and Technology(MNIST)handwritten digits,which is attributed to the enhanced linearity and higher uniformity of LTP/LTD characteristics.展开更多
基金supported by Post-Moore Major Project of the National Natural Science Foundation of China(Grant No.92364204)Zhejiang Province introduces and cultivates leading innovation and entrepreneurship teams(Grant No.2023R01011)+1 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LMS25F040005)the Key R&D Program of Zhejiang(Grant No.2024SSYS0042)。
文摘Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for artificial intelligence sensing.For instance,the incorporation of low-dimensional materials(e.g.,quantum dots,carbon nanotubes,and two-dimensional materials)optimizes device optoelectronic properties,while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor(CMOS)compatibility.Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption,offering a new paradigm for highly integrated,energy-efficient real-time perception.However,critical challenges—including device non-uniformity caused by material interface defects,system instability induced by memristor conductance drift,and environmental adaptability under complex illumination—remain barriers to scalable applications.This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure,operational mechanisms,materials,and applications.It explores the pivotal roles of memristors,electrolyte-gated transistors,and other neuromorphic devices in optical signal perception and information processing,with a focus on their implementations in visual perception tasks and future prospects.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051,ZR2025QB50)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2025A1515011191)the Shanghai Sailing Program(23YF1402200,23YF1402400)funded by Basic Research Program of Jiangsu(BK20240424)Open Research Fund of State Key Laboratory of Crystal Materials(KF2406)Taishan Scholar Foundation of Shandong Province(tsqn202408006,tsqn202507058)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University。
文摘As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.
基金supported by the National Natural Science Foundation of China(Grant No.62171182)the Natural Scienceof Hunan Province(Grant No.2025JJ50345)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20240452)。
文摘The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.
基金the financial support from the National Key Research and Development Program of China(Grant No.2022YFB4400100)the NSFC under Grant Nos.92477102 and 62122084the open research fund of Songshan Lake Materials Laboratory 2023SLABFK09。
文摘The increasing complexity of intelligent sensing environments,driven by the growth of Internet of Things technologies,has created a strong demand for neuromorphic systems capable of real-time,low-power multisensory perception.Traditional sensory architectures,constrained by single-modal processing and centralized computing,struggle to meet the requirements of diverse and dynamic input conditions.Multisensory neuromorphic devices offer a promising solution by mimicking the distributed,event-driven processing of biological systems.Recent efforts have explored synaptic devices and material systems that respond to various input modalities,including visual,tactile,thermal,and chemical stimuli.However,challenges remain in signal conversion,encoding compatibility,and the fusion of heterogeneous inputs without loss of unisensory information.This review provides a comprehensive overview of the physical mechanisms,device behaviors,and integration strategies that underpin signal processing in neuromorphic hardware.We highlight synaptic mechanisms conducive to cross-modal interaction,analyze representative signal fusion approaches at the device level,and discuss future directions for constructing efficient,scalable,and biologically inspired multisensory neuromorphic systems.
基金financially supported by the National Natural Science Foundation of China(Grant No.12172093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607)。
文摘High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Natural Science Foundation of Jiangsu Province(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.
基金supported by the National Natural Science Foundation of China(nos.62461160330,62304021,and 62404018)the Hebei Natural Science Foundation(F2024105006),the China Postdoctoral Science Foundation(2024T171120 and 2023M740232)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(GZB20230932).
文摘All-optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption,meeting the application demands of the current artificial intelligence era.However,developing all-optically controlled devices that combine high performance and high reproducibility remains a significant challenge.In this work,we demonstrate an all-optically controlled artificial synapse based on ZnO and Cs_(2)CoCl_(4)single crystal connected structure,which integrates light information sensing and processing capabilities.Relying on the simple series-connected structure,as well as the positive photoconductance of ZnO and the negative photoconductance of Cs_(2)CoCl_(4),the optically controlled bidirectional synaptic plasticity is realized under ultraviolet and visible light stimulation without additional voltage modulation in the all-optically controlled synapse.In addition,leveraging its ultraviolet-enhanced feature extraction and visible-suppression capabilities,the all-optically controlled synapse can act as denoising units in bioinformation preprocessing and weight-updating units in feature recognition.The proposed all-optically controlled synapses exhibit excellent information perception,low-level noise reduction,and high-level cognition functions for bioinformation recognition under complex light conditions.We believe that this work can provide structural-level insights and inspirations in the design and fabrication of all-optically controlled synapses to promote the application for efficient neuromorphic vision in the future.
基金support from the Kreitman School of Advanced Graduate Studies, Ben-Gurion University of the Negev。
文摘Neuromorphic cameras,or dynamic vision sensors,are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level.Recently,they garnered increasing interest due to their extremely high temporal resolution,wide dynamic range,low power consumption,and high pixel bandwidth.Despite their advantages,most existing three-dimensional (3D) event imaging solutions rely on multicamera configurations,which are costly,complex,and challenging to synchronize.In this study,we introduce a new framework for four-dimensional (4D) event imaging using a single static neuromorphic camera.We take advantage of the inherent sparsity of event data to combine optically encoded stereo channels into a single event camera.By utilizing optical channel multiplexing,we maintain sensor resolution while retaining the key advantages of event cameras.
基金funded by National Natural Science Foundation of China(No.62474144,62501489,T2222025,and 62174053)Basic Research Program of Jiangsu(No.BK20250465)+4 种基金Centrally Guided Local Science and Technology Development Fund(2024ZYZX4025)Suzhou Industrial Park MEMS Advanced High-Performance Sensor Chip Technology Transfer Platform(CXZ2024201)Jiangsu Data Science and Cognitive Computational Engineering Research CentreXJTLU AI University Research CentreXJTLU Advanced Materials Research Center。
文摘Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulation toward dynamic,network-oriented plasticity design,offering enhanced computational accuracy and functional relevance.This review highlights how diversified plasticity behaviors,including multilevel long-term potentiation and depression for spatial models,tunable short-term memory for temporal models,as well as wavelength-selective response,excitatory and inhibitory synergy,and adaptive threshold modulation,collectively support key tasks such as stable learning,temporal processing,and context-aware adaptation.Beyond behavioral innovations,strategies such as multifunctional single-device integration,multimodal fusion,and heterogeneous system assembly enable compact,energy-efficient,and versatile neuromorphic architectures.Recent developments at the array level further demonstrate high-performance scalability and system-level applicability.Despite notable progress,current modulation strategies remain constrained in flexibility,diversity,and large-scale coordination.Future research should focus on enriching the behavioral repertoire of plasticity,advancing crossmodal convergence,and improving array-level uniformity,paving the way toward deployable,high-efficiency neuromorphic intelligence.
基金financially supported by the Ministry of Education(Singapore)(MOE-T2EP50220-0022)SUTD-MIT International Design Center(Singapore)+3 种基金SUTD-ZJU IDEA Grant Program(SUTD-ZJU(VP)201903)SUTD Kickstarter Initiative(SKI 2021_02_03,SKI 2021_02_17,SKI 2021_01_04)Agency of Science,Technology and Research(Singapore)(A20G9b0135)National Supercomputing Centre(Singapore)(15001618)。
文摘Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.
基金supported by the National Key R&D Program of China(2021YFB3601000,2021YFB3601004)the National Key R&D Program of China(2022YFB3604702)the Chinese Academy of Sciences.
文摘In recent years,research focusing on synaptic device based on phototransistors has provided a new method for asso-ciative learning and neuromorphic computing.A TiO_(2)/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured,integrating a TiO_(2)nanolayer gate and a two-dimensional electron gas(2DEG)channel to mimic the synaptic weight and the synaptic cleft,respectively.The maximum drain to source current is 10 nA,while the device is driven at a reverse bias not exceeding-2.5 V.A excitatory postsynaptic current(EPSC)of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35μW·cm^(-2).Multiple synaptic neuromorphic functions,including EPSC,short-term/long-term plasticity(STP/LTP)and paried-pulse facilitation(PPF),are effectively mimicked by our GaN-based het-erostructure synaptic device.In the typical Pavlov’s dog experiment,we demonstrate that the device can achieve"retraining"process to extend memory time through enhancing the intensity of synaptic weight,which is similar to the working mecha-nism of human brain.
基金supported by National Natural Science Foundation of China(62174164,U23A20568,and U22A2075)National Key Research and Development Project(2021YFA1202600)+2 种基金Talent Plan of Shanghai Branch,Chinese Academy of Sciences(CASSHB-QNPD-2023-022)Ningbo Technology Project(2022A-007-C)Ningbo Key Research and Development Project(2023Z021).
文摘The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.
基金supported by the Natural Science Foundation of Zhejiang Province(Grant No.LQ24F040007)the National Natural Science Foundation of China(Grant No.U22A2075)the Opening Project of State Key Laboratory of Polymer Materials Engineering(Sichuan University)(Grant No.sklpme2024-1-21).
文摘To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.
基金supported by the Hunan Science Fund for Distinguished Young Scholars(2023JJ10069)the National Natural Science Foundation of China(52172169)the Project of State Key Laboratory of Precision Manufacturing for Extreme Service Performance,Central South University(ZZYJKT2024-02).
文摘The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromorphic comput-ing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence.Among various neuromorphic devices,the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consump-tion,multimodal sensing/recording capabilities,and multifunctional integration.Moreover,the emerging optoelectronic neuro-morphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neu-romorphic computing field.Therefore,this article reviews recent advancements in electrolyte-gated optoelectronic neuromor-phic transistors.First,it provides an overview of artificial optoelectronic synapses and neurons,discussing aspects such as device structures,operating mechanisms,and neuromorphic functionalities.Next,the potential applications of optoelectronic synapses in different areas such as artificial visual system,pain system,and tactile perception systems are elaborated.Finally,the current challenges are summarized,and future directions for their developments are proposed.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant No.12425209)the National Natural Science Foundation of China(Grant No.U20A20390,11827803,12172034,11822201,62004056,62104058,62271269).
文摘Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption.MXene-Ti_(3)C_(2)T_(x),an emerging twodimensional material,stands out as an ideal candidate for fabricating neuromorphic devices.Its exceptional electrical performance and robust mechanical properties make it an ideal choice for this purpose.This review aims to uncover the advantages and properties of MXene-Ti_(3)C_(2)T_(x)in neuromorphic devices and to promote its further development.Firstly,we categorize several core physical mechanisms present in MXene-Ti_(3)C_(2)T_(x)neuromorphic devices and summarize in detail the reasons for their formation.Then,this work systematically summarizes and classifies advanced techniques for the three main optimization pathways of MXene-Ti_(3)C_(2)T_(x),such as doping engineering,interface engineering,and structural engineering.Significantly,this work highlights innovative applications of MXene-Ti_(3)C_(2)T_(x)neuromorphic devices in cutting-edge computing paradigms,particularly near-sensor computing and in-sensor computing.Finally,this review carefully compiles a table that integrates almost all research results involving MXene-Ti_(3)C_(2)T_(x)neuromorphic devices and discusses the challenges,development prospects,and feasibility of MXene-Ti_(3)C_(2)T_(x)-based neuromorphic devices in practical applications,aiming to lay a solid theoretical foundation and provide technical support for further exploration and application of MXene-Ti_(3)C_(2)T_(x)in the field of neuromorphic devices.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Funded by Basic Research Program of Jiangsu(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘As an emerging memory device,memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption.This review paper focuses on the application of low-power-based memristors in various aspects.The concept and structure of memristor devices are introduced.The selection of functional materials for low-power memristors is discussed,including ion transport materials,phase change materials,magnetoresistive materials,and ferroelectric materials.Two common types of memristor arrays,1T1R and 1S1R crossbar arrays are introduced,and physical diagrams of edge computing memristor chips are discussed in detail.Potential applications of low-power memristors in advanced multi-value storage,digital logic gates,and analogue neuromorphic computing are summarized.Furthermore,the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.
基金supported by the Joint R&D Fund of Beijing Smartchip Microelectronics Technology Co.,Ltd.,SGSC0000XSQT2207067.
文摘As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain's neural networks.Volatile memristors,particularly Mott and diffusive memristors,have garnered significant attention for their ability to emulate neuronal dynamics,such as spiking and firing patterns,enabling the development of reconfigurable and adaptive computing systems.Recent advancements include the implementation of leaky integrate-and-fire neurons,Hodgkin-Huxley neurons,opto-electronic neurons,and time-surface neurons,all utilizing volatile memristors to achieve efficient,low-power,and highly inte-grated neuromorphic systems.This paper reviews the latest progress in volatile memristor-based artificial neurons,highlight-ing their potential for energy-efficient computing and integration with artificial synapses.We conclude by addressing chal-lenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic com-puting.
基金the financial support given by National Natural Science Foundation of China(52227808,62202285)the National Science Foundation for Distinguished Young Scholars of China(51725505)+1 种基金the Development Fund for Shanghai Talents(No.2021003)Shanghai Collaborative Innovation Center of Intelligent Perception Chip Technology。
文摘Neuromorphic devices,inspired by the intricate architecture of the human brain,have garnered recognition for their prodigious computational speed and sophisticated parallel computing capabilities.Vision,the primary mode of external information acquisition in living organisms,has garnered substantial scholarly interest.Notwithstanding numerous studies simulating the retina through optical synapses,their applications remain circumscribed to single-mode perception.Moreover,the pivotal role of temperature,a fundamental regulator of biological activities,has regrettably been relegated to the periphery.To address these limitations,we proffer a neuromorphic device endowed with multimodal perception,grounded in the principles of light-modulated semiconductors.This device seamlessly accomplishes dynamic hybrid visual and thermal multimodal perception,featuring temperature-dependent paired pulse facilitation properties and adaptive storage.Crucially,our meticulous examination of transfer curves,capacitance–voltage(C–V)tests,and noise measurements provides insights into interface and bulk defects,elucidating the physical mechanisms underlying adaptive storage and other functionalities.Additionally,the device demonstrates a variety of synaptic functionalities,including filtering properties,Ebbinghaus curves,and memory applications in image recognition.Surprisingly,the digital recognition rate achieves a remarkable value of 98.8%.
基金supported by the National Key Research and Development Program of China(2020YFA0714700)Natural Science Foundation of China(62274174)+3 种基金Key Research and Development Program of Jiangsu Province(BK20232009)a fellowship from the China Postdoctoral Science Foundation(NO:2023M742559)the Cooperation Project of Vacuum Interconnect Research Facility(NANO-X)of Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences(F2208)the technical support for Nano-X from Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences(SINANO)。
文摘Artificial multisensory devices play a key role in human-computer interaction in the field of artificial intelligence(AI).In this work,we have designed and constructed a novel olfactory-visual bimodal neuromorphic carbon nanotube thin film transistor(TFT)arrays for artificial olfactory-visual multisensory synergy recognition with a very low power consumption of 25 aJ for a single pulse,employing semiconducting single-walled carbon nanotubes(sc-SWCNTs)as channel materials and gas sensitive materials,and poly[[4,8-bis[5-(2-ethylhexyl)-2-thienyl]benzo[1,2-b:4,5-b0]dithiophene-2,6-diyl]-2,5-thiophenediyl-[5,7-bis(2-ethylhexyl)-4,8-dioxo-4H,8H-benzo[1,2-c:4,5-c0]dithio-phene-1,3-diyl]](PBDB-T)as the photosensitive material.It is noted that it is the first time to realize the simulation of olfactory and visual senses(from 280 nm to 650 nm)with the wide operating temperature range(0-150℃)in a single SWCNT TFT device and successfully simulate the recovery of olfactory senses after COVID-19 by olfactory-visual synergy.Furthermore,our SWCNT neuromorphic TFT devices with a high IOn/IOff ratio(up to 10^(6))at a low operating voltage(−2 to 0.5 V)can mimic not only the basic biological synaptic functions of olfaction and vision(such as paired-pulse facilitation,short-term plasticity,and long-term plasticity),but also optical wireless communication by Morse code.The proposed multisensory,broadband light-responsive,low-power synaptic devices provide great potential for developing AI robots to face complex external environments.
基金supported by the Singapore Ministry of Educa-tion under Research(Grant no.MOE-T2EP50120-0003).
文摘Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of short-term and long-term synaptic plasticity behaviors of the biological synapses is an es-sential step.However,the electronic synaptic devices suffer from higher variability issues which hinder the application of such devices to build neuromorphic systems.For practical applications,it is essen-tial to minimize the cycle-to-cycle and device-to-device variations in the synaptic functions of artifi-cial electronic synapses.This study involves the fabrication of diffusive memristor devices using WTe_(2) chalcogenide as the main switching material.The choice of the switching material provides a facile so-lution to the variability problem.The greater uniformity in the switching characteristics of the WTe_(2)-based memristor offers higher uniformity for the synaptic emulation.These devices exhibit both volatile and nonvolatile switching properties,allowing them to emulate both short-term and long-term synaptic functions.The WTe_(2)-based electronic synaptic devices present a high degree of uniformity for the emula-tion of various essential biological synaptic functions including short-term potentiation(STP),long-term potentiation(LTP),long-term depression(LTD),spike-rate-dependent plasticity(SRDP),and spike-timing-dependent plasticity(STDP).A higher recognition accuracy of∼92%is attained for pattern recognition using the modified National Institute of Standards and Technology(MNIST)handwritten digits,which is attributed to the enhanced linearity and higher uniformity of LTP/LTD characteristics.
