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 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
Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics,optics,and electronics.Among different types of heterostructures,the gr...Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics,optics,and electronics.Among different types of heterostructures,the gradient one may provide multiple resistive states and immobilized conductive fila-ments,offering great prospect for fabricating memristors with both high neuromorphic computation capability and repeatability.Here,we invent a memristor based on a homologous gradient heterostructure(HGHS),compris-ing a conductive transition metal dichalcogenide and an insulating homolo-gous metal oxide.Memristor made of Ta–TaS_(x)O_(y)–TaS 2 HGHS exhibits continuous potentiation/depression behavior and repeatable forward/backward scanning in the read-voltage range,which are dominated by multi-ple resistive states and immobilized conductive filaments in HGHS,respec-tively.Moreover,the continuous potentiation/depression behavior makes the memristor serve as a synapse,featuring broad-frequency response(10^(-1)–10^(5) Hz,covering 106 frequency range)and multiple-mode learning(enhanced,depressed,and random-level modes)based on its natural and moti-vated forgetting behaviors.Such HGHS-based memristor also shows good unifor-mity for 5?7 device arrays.Our work paves a way to achieve high-performance integrated memristors for future artificial neuromorphic computation.展开更多
The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisen...The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisensory signal generation and neuroplasticity-based signal processing,here,an artificial perceptual neuro array with visual-tactile sensing,processing,learning,and memory is demonstrated.The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer,endowing individual and synergistic plastic modulation of optical and mechanical information,including short-term memory,long-term memory,paired pulse facilitation,and“learning-experience”behavior.Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of“Pavlov's dog”.The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process.A machine-learning algorithm is coupled with an artificial neural network for pattern recognition,achieving a recognition accuracy of 70%for bimodal training,which is higher than that obtained by unimodal training.In addition,the artificial perceptual neuron has a low energy consumption of~20 pJ.With its mechanical compliance and simple architecture,the neuromorphic bimodal perception array has promising applications in largescale cross-modal interactions and high-throughput intelligent perceptions.展开更多
Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelect...Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide(ZnO-ITO/WO_(3-x))heterojunctions as a promising solution.Through applying different types of electrical and optical signals,the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity,alongside short-term and long-term memory.Introducing the ZnO-ITO functional layer enhances the photoresponse of the WO_(3-x)-based memristor and demonstrates“learning-forgetting-relearning”behavior under optical modulation.Furthermore,based on the photoelectric cooperative memristor array,a convolutional neural network for vehicle type recognition is constructed,which solves the problem of zero weight and negative weight complexity.In regard to energy efficiency,the neural network built with this device operates at a power level of only 10^(-3)W,representing a reduction of more than 4 orders of magnitude compared with a standard central processor.Hence,the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.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
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
Inspired by the visual neurons of biological systems,optoelectronic synaptic devices integrate photoresponsive semiconductor materials to convert light into electrical signals,enabling biomimetic visual perception sys...Inspired by the visual neurons of biological systems,optoelectronic synaptic devices integrate photoresponsive semiconductor materials to convert light into electrical signals,enabling biomimetic visual perception systems.Achieving memory retention and intelligent perceptual functions continues to pose a major hurdle in the advancement of neuromorphic artificial synapse devices.This review begins with an exploration of biological neural synapses,analyzing the fundamental characteristics and structures of biomimetic optoelectronic synapses.It then delves into the design of device and material structures to achieve postsynaptic current and memory behavior,elucidating their underlying mechanisms.Furthermore,the latest application scenarios of these devices are summarized,highlighting the opportunities and challenges in their future development.This review aims to provide a comprehensive understanding of the advancements in optoelectronic synapses,from material innovations to neuromorphic applications,paving the way for next-generation artificial visual systems and neuromorphic computing.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
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
To present an advanced device scheme of high-performance optoelectronic synapses,herein,we demonstrated the electrically-and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-bas...To present an advanced device scheme of high-performance optoelectronic synapses,herein,we demonstrated the electrically-and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-based ferroelectric field-effect transistor(FeFET)architecture.The device was fabricated in the form of the MoS_(2)/PZT FeFET,and its synaptic weights were effectively controlled by dual stimuli(i.e.,both electrical and optical pulses simultaneously)as well as single stimuli(i.e.,either electrical or optical pulses alone).This could be attributed to the electrical pulse-tunable strong ferroelectric polarization in PbZrxTi_(1−x)O_(3)(PZT)as well as the polarization field-enhanced persistent photoconductivity effect in MoS_(2).Additionally,it was confirmed that the proposed device possesses substantial activity,achieving approximately 95%pattern recognition accuracy.The results substantiate the great potential of the 2D semiconductor channel-based FeFET device as a high-performance optoelectronic synaptic platform,marking a pivotal stride towards the realization of advanced neuromorphic computing systems.展开更多
Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilaye...Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilayer Pt/HfO_(2)/ZrO_(2-x)/HfO_(2)/TiN memristor,engineered with a ZrO_(2-x) oxygen vacancy reservoir(OVR)layer fabricated via radio frequency(RF)sputtering under controlled oxygen ambient.The incorporation of the ZrO_(2-x) OVR layer enables enhanced resistive switching characteristics,including a high ON/OFF ratio(∼8000),excellent uniformity,robust data retention(>105 s),and multilevel storage capabilities.Furthermore,the memristor demonstrates superior synaptic plasticity with linear long-term potentiation(LTP)and depression(LTD),achieving low non-linearity values of 1.36(LTP)and 0.66(LTD),and a recognition accuracy of 95.3%in an MNIST dataset simulation.The unique properties of the ZrO_(2-x) layer,particularly its ability to act as a dynamic oxygen vacancy reservoir,significantly enhance synaptic performance by stabilizing oxygen vacancy migration.These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.展开更多
The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language mod...The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and pow...Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.展开更多
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We thank the financial support from the National Science Fund for Distinguished Young Scholars(No.52125309)the National Natural Science Foundation of China(Nos.51991343,52188101,51920105002,and 51991340)+1 种基金Guang-dong Innovative and Entrepreneurial Research Team Pro-gram(No.2017ZT07C341)the Shenzhen Basic Research Program(Nos.JCYJ20200109144616617 and JCYJ20200109144620815)。
文摘Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics,optics,and electronics.Among different types of heterostructures,the gradient one may provide multiple resistive states and immobilized conductive fila-ments,offering great prospect for fabricating memristors with both high neuromorphic computation capability and repeatability.Here,we invent a memristor based on a homologous gradient heterostructure(HGHS),compris-ing a conductive transition metal dichalcogenide and an insulating homolo-gous metal oxide.Memristor made of Ta–TaS_(x)O_(y)–TaS 2 HGHS exhibits continuous potentiation/depression behavior and repeatable forward/backward scanning in the read-voltage range,which are dominated by multi-ple resistive states and immobilized conductive filaments in HGHS,respec-tively.Moreover,the continuous potentiation/depression behavior makes the memristor serve as a synapse,featuring broad-frequency response(10^(-1)–10^(5) Hz,covering 106 frequency range)and multiple-mode learning(enhanced,depressed,and random-level modes)based on its natural and moti-vated forgetting behaviors.Such HGHS-based memristor also shows good unifor-mity for 5?7 device arrays.Our work paves a way to achieve high-performance integrated memristors for future artificial neuromorphic computation.
基金National Natural Science Foundation of China,Grant/Award Numbers:52002246,52192614,U22A2077,U20A20166,52125205,52372154Natural Science Foundation of Beijing Municipality,Grant/Award Numbers:2222088,Z180011+4 种基金Shenzhen Fundamental Research Project,Grant/Award Number:JCYJ20190808170601664Shenzhen Science and Technology Program,Grant/Award Number:KQTD20170810105439418Science and Technology Innovation Project of Shenzhen Excellent Talents,Grant/Award Number:RCBS20200714114919006National Key R&D Program of China,Grant/Award Numbers:2021YFB3200304,2021YFB3200302Fundamental Research Funds for the Central Universities。
文摘The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisensory signal generation and neuroplasticity-based signal processing,here,an artificial perceptual neuro array with visual-tactile sensing,processing,learning,and memory is demonstrated.The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer,endowing individual and synergistic plastic modulation of optical and mechanical information,including short-term memory,long-term memory,paired pulse facilitation,and“learning-experience”behavior.Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of“Pavlov's dog”.The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process.A machine-learning algorithm is coupled with an artificial neural network for pattern recognition,achieving a recognition accuracy of 70%for bimodal training,which is higher than that obtained by unimodal training.In addition,the artificial perceptual neuron has a low energy consumption of~20 pJ.With its mechanical compliance and simple architecture,the neuromorphic bimodal perception array has promising applications in largescale cross-modal interactions and high-throughput intelligent perceptions.
基金supported by the National Natural Science Foundation of China(62174068,62311540155,62174068,and 61804063)Jinan City-University Integrated Development Strategy Project(JNSX2023017)+2 种基金Taishan Scholars Project Special Funds(tsqn202312035)the National Key Research and Development Program of China(2019YFA0705900)funded by MOSTthe Natural Science Foundation of Jilin Province(20220201070GX)。
文摘Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide(ZnO-ITO/WO_(3-x))heterojunctions as a promising solution.Through applying different types of electrical and optical signals,the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity,alongside short-term and long-term memory.Introducing the ZnO-ITO functional layer enhances the photoresponse of the WO_(3-x)-based memristor and demonstrates“learning-forgetting-relearning”behavior under optical modulation.Furthermore,based on the photoelectric cooperative memristor array,a convolutional neural network for vehicle type recognition is constructed,which solves the problem of zero weight and negative weight complexity.In regard to energy efficiency,the neural network built with this device operates at a power level of only 10^(-3)W,representing a reduction of more than 4 orders of magnitude compared with a standard central processor.Hence,the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.
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 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financially supported by the National Key Research and Development Program of China(Nos.2022YFA1204500 and 2022YFA1204502)the National Natural Science Foundation of China(Nos.22293043 and 92163209)the IPE Project for Frontier Basic Research,China(No.QYJC-2023-08)
文摘Inspired by the visual neurons of biological systems,optoelectronic synaptic devices integrate photoresponsive semiconductor materials to convert light into electrical signals,enabling biomimetic visual perception systems.Achieving memory retention and intelligent perceptual functions continues to pose a major hurdle in the advancement of neuromorphic artificial synapse devices.This review begins with an exploration of biological neural synapses,analyzing the fundamental characteristics and structures of biomimetic optoelectronic synapses.It then delves into the design of device and material structures to achieve postsynaptic current and memory behavior,elucidating their underlying mechanisms.Furthermore,the latest application scenarios of these devices are summarized,highlighting the opportunities and challenges in their future development.This review aims to provide a comprehensive understanding of the advancements in optoelectronic synapses,from material innovations to neuromorphic applications,paving the way for next-generation artificial visual systems and neuromorphic computing.
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.
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 Research Foundation(NRF)of Korea through the Basic Science Research Programs(Nos.2019R1A2C1085448,2023R1A2C1005421,RS-2024-00356939)funded by the Korean Government.
文摘To present an advanced device scheme of high-performance optoelectronic synapses,herein,we demonstrated the electrically-and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-based ferroelectric field-effect transistor(FeFET)architecture.The device was fabricated in the form of the MoS_(2)/PZT FeFET,and its synaptic weights were effectively controlled by dual stimuli(i.e.,both electrical and optical pulses simultaneously)as well as single stimuli(i.e.,either electrical or optical pulses alone).This could be attributed to the electrical pulse-tunable strong ferroelectric polarization in PbZrxTi_(1−x)O_(3)(PZT)as well as the polarization field-enhanced persistent photoconductivity effect in MoS_(2).Additionally,it was confirmed that the proposed device possesses substantial activity,achieving approximately 95%pattern recognition accuracy.The results substantiate the great potential of the 2D semiconductor channel-based FeFET device as a high-performance optoelectronic synaptic platform,marking a pivotal stride towards the realization of advanced neuromorphic computing systems.
基金financially supported by the National Research Foundation of Korea(no.NRF-2021R1A2C2010781)grant funded by the Korean Government(Ministry of Science and ICT)Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(no.P0012451,The Competency Development Program for Industry Specialist)Korea Government(MOTIE)(no.P0020966,HRD Program for Industrial Innovation).
文摘Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilayer Pt/HfO_(2)/ZrO_(2-x)/HfO_(2)/TiN memristor,engineered with a ZrO_(2-x) oxygen vacancy reservoir(OVR)layer fabricated via radio frequency(RF)sputtering under controlled oxygen ambient.The incorporation of the ZrO_(2-x) OVR layer enables enhanced resistive switching characteristics,including a high ON/OFF ratio(∼8000),excellent uniformity,robust data retention(>105 s),and multilevel storage capabilities.Furthermore,the memristor demonstrates superior synaptic plasticity with linear long-term potentiation(LTP)and depression(LTD),achieving low non-linearity values of 1.36(LTP)and 0.66(LTD),and a recognition accuracy of 95.3%in an MNIST dataset simulation.The unique properties of the ZrO_(2-x) layer,particularly its ability to act as a dynamic oxygen vacancy reservoir,significantly enhance synaptic performance by stabilizing oxygen vacancy migration.These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.
文摘The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金Project supported by the National Key Research and Development Program of China(Grant No.2024YFA1208800)the National Natural Science Foundation of China(Grant Nos.62404253,62304254,U23A20322)。
文摘Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.
