Optical synapses have an ability to perceive and remember visual information,making them expected to provide more intelligent and efficient visual solutions for humans.As a new type of artificial visual sensory device...Optical synapses have an ability to perceive and remember visual information,making them expected to provide more intelligent and efficient visual solutions for humans.As a new type of artificial visual sensory devices,photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision.However,due to the urgent problems of nonlinear conductance and high-energy consumption,its further application in high-precision control scenarios and integration is hindered.In this work,we report an optoelectronic memristor with a structure of TiN/CeO_(2)/ZnO/ITO/Mica,which can achieve minimal energy consumption(187 pJ)at a single pulse(0.5 V,5 ms).Under the stimulation of continuous pulses,linearity can be achieved up to 99.6%.In addition,the device has a variety of synaptic functions under the combined action of photoelectric,which can be used for advanced vision.By utilizing its typical long-term memory characteristics,we achieved image recognition and long-term memory in a 3×3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%.Moreover,we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform.This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems,offering a new direction for bionic eyes and visual automation technology.展开更多
Recent advances in all-inorganic perovskite semiconductors have garnered significant research interest due to their potential for high-performance optoelectronic devices and enhanced stability under harsh environmenta...Recent advances in all-inorganic perovskite semiconductors have garnered significant research interest due to their potential for high-performance optoelectronic devices and enhanced stability under harsh environmental conditions.A deeper understanding of their structural,chemical,and physical properties has driven notable progress in addressing challenges related to electrical characteristics,reproducibility,and long-term operational stability in perovskite-based memristors.These advancements have been realized through composition engineering,dimensionality modulation,thin-film processing,and device optimization.This review concisely summarizes recent developments in all-inorganic perovskite memristors,highlighting their diverse material properties,device performance,and applications in artificial synapses and logic operations.We discuss key resistance-switching mechanisms,optimization strategies,and operational capabilities while outlining remaining challenges and future directions for perovskitebased memory technologies.展开更多
Despite great advancements in organic mixed ionic-electronic conductors(OMIECs),their applications remain predominantly restricted to three-electrode organic electro-chemical transistors(OECTs),which rely on an additi...Despite great advancements in organic mixed ionic-electronic conductors(OMIECs),their applications remain predominantly restricted to three-electrode organic electro-chemical transistors(OECTs),which rely on an additional electrolyte layer to balance ionic and electronic transport,resulting in indirect coupling of charge carriers.While direct coupling has the potential to greatly simplify device architectures,it remains underexplored in OMIECs due to the inherent imbalance between electronic and ionic conductivities.In this study,we introduce a straightforward approach to achieve balanced OMIECs and employ them as channel materials in two-electrode organic electrochemical memristors.These devices provide clear evidence of direct coupling between electronic and ionic carriers and exhibit exceptional performance in synaptic device applications.Our findings offer new insights into charge carrier transport mechanisms in OMIECs and establish organic electrochemical memristors as a promising new class of organic electronic devices for next-generation neuromorphic applications.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
With the exponential growth of the internet of things,artificial intelligence,and energy-efficient high-volume data digital communications,there is an urgent demand to develop new information technologies with high st...With the exponential growth of the internet of things,artificial intelligence,and energy-efficient high-volume data digital communications,there is an urgent demand to develop new information technologies with high storage capacity.This needs to address the looming challenge of conventional Von Neumann architecture and Moore's law bottleneck for future data-intensive computing applications.A promising remedy lies in memristors,which offer distinct advantages of scalability,rapid access times,stable data retention,low power consumption,multistate storage capability and fast operation.Among the various materials used for active layers in memristors,low dimensional perovskite semiconductors with structural diversity and superior stability exhibit great potential for next generation memristor applications,leveraging hysteresis characteristics caused by ion migration and defects.In this review the progress of low-dimensional perovskite memory devices is comprehensively summarized.The working mechanism and fundamental processes,including ion migration dynamics,charge carrier transport and electronic resistance that underlies the switching behavior of memristors are discussed.Additionally,the device parameters are analyzed with special focus on the effective methods to improve electrical performance and operational stability.Finally,the challenges and perspective on major hurdles of low-dimensional perovskite memristors in the expansive application domains are provided.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
Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex asso...Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.展开更多
Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligen...Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.展开更多
Owing to the advantages of simple structure,low power consumption and high-density integration,memristors or memristive devices are attracting increasing attention in the fields such as next generation non-volatile me...Owing to the advantages of simple structure,low power consumption and high-density integration,memristors or memristive devices are attracting increasing attention in the fields such as next generation non-volatile memories,neuromorphic computation and data encryption.However,the deposition of memristive films often requires expensive equipment,strict vacuum conditions,high energy consumption,and extended processing times.In contrast,electrochemical anodizing can produce metal oxide films quickly(e.g.10 s) under ambient conditions.By means of the anodizing technique,oxide films,oxide nanotubes,nanowires and nanodots can be fabricated to prepare memristors.Oxide film thickness,nanostructures,defect concentrations,etc,can be varied to regulate device performances by adjusting oxidation parameters such as voltage,current and time.Thus memristors fabricated by the anodic oxidation technique can achieve high device consistency,low variation,and ultrahigh yield rate.This article provides a comprehensive review of the research progress in the field of anodic oxidation assisted fabrication of memristors.Firstly,the principle of anodic oxidation is introduced;then,different types of memristors produced by anodic oxidation and their applications are presented;finally,features and challenges of anodic oxidation for memristor production are elaborated.展开更多
Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation...Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.展开更多
New neuromorphic architectures and memory technologies with low power consumption,scalability and high-speed are in the spotlight due to the von Neumann bottleneck and limitations of Moore’s law.The memristor,a two-t...New neuromorphic architectures and memory technologies with low power consumption,scalability and high-speed are in the spotlight due to the von Neumann bottleneck and limitations of Moore’s law.The memristor,a two-terminal synaptic device,shows powerful capabilities in neuromorphic computing and information storage applications.Active materials with high defect migration speed and low defect migration barrier are highly promising for high-performance memristors.Halide perovskite(HP)materials with point defects(such as gaps,vacancies,and inversions)have strong application potential in memristors.In this article,we review recent advances on HP memristors with exceptional performances.First,the working mechanisms of memristors are described.Then,the structures and properties of HPs are explained.Both electrical and photonic HP-based memristors are overviewed and discussed.Different fabrication methods of HP memristor devices and arrays are described and compared.Finally,the challenges in integrating HP memristors with complementary metal oxide semiconductors(CMOS)are briefly discussed.This review can assist in developing HP memristors for the next-generation information technology.展开更多
Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low po...Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.展开更多
Organic-inorganic halides perovskites(OHPs)have drawn the attention of many researchers owing to their astonishing and unique optoelectronic properties.They have been extensively used for photovoltaic applications,ach...Organic-inorganic halides perovskites(OHPs)have drawn the attention of many researchers owing to their astonishing and unique optoelectronic properties.They have been extensively used for photovoltaic applications,achieving higher than 26%power conversion efficiency to date.These materials have potential to be deployed for many other applications beyond photovoltaics like photodetectors,sensors,light-emitting diodes(LEDs),and resistors.To address the looming challenge of Moore’s law and the Von Neumann bottleneck,many new technologies regarding the computation of architectures and storage of information are being extensively researched.Since the discovery of the memristor as a fourth component of the circuit,many materials are explored for memristive applications.Lately,researchers have advanced the exploration of OHPs for memristive applications.These materials possess promising memristive properties and various kinds of halide perovskites have been used for different applications that are not only limited to data storage but expand towards artificial synapses,and neuromorphic computing.Herein we summarize the recent advancements of OHPs for memristive applications,their unique electronic properties,fabrication of materials,and current progress in this field with some future perspectives and outlooks.展开更多
In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emula...In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.展开更多
The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerg...The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.展开更多
Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuro...Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.展开更多
As the fourth passive circuit component, a memristor is a nonlinear resistor that can ＂remember＂ the amount of charge passing through it. The characteristic of ＂remembering＂ the charge and non-volatility makes mem...As the fourth passive circuit component, a memristor is a nonlinear resistor that can ＂remember＂ the amount of charge passing through it. The characteristic of ＂remembering＂ the charge and non-volatility makes memristors great potential candidates in many fields. Nowadays, only a few groups have the ability to fabricate memristors, and most researchers study them by theoretic analysis and simulation. In this paper, we first analyse the theoretical base and characteristics of memristors, then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics. Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.展开更多
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses ...Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear drift.Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.展开更多
As an alternative device for neuromorphic computing to conquer von Neumann bottleneck,the memristor serving as an artificial synapse has attracted much attention.The TaO^(x) memristors embedded with silver nanoparticl...As an alternative device for neuromorphic computing to conquer von Neumann bottleneck,the memristor serving as an artificial synapse has attracted much attention.The TaO^(x) memristors embedded with silver nanoparticles(Ag NPs)have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs.The TaO^(x) memristors with and without Ag NPs are capable of simulating synaptic plasticity(PTP,STDP,and STP to LTP),learning,and memory behaviors.The conduction of the high resistance state(HRS) is driven by Schottky-emission mechanism.The embedment of Ag NPs causes the low resistance state(LRS) conduction governed by a Poole-Frenkel emission mechanism instead of a space-charge-limited conduction(SCLC) in a pure TaO^(x) system,which is ascribed to the Ag NPs enhancing electric field to produce additional traps and to reduce Coulomb potential energy of bound electrons to assist electron transport.Consequently,the enhanced electric fields induced by Ag NPs increase the learning strength and learning speed of the synapses.Additionally,they also improve synaptic sensitivity to stimuli.The linearity of conductance modulation and the reproducibility of conductance are improved as well.展开更多
基金supported by Science and Technology Project of Hebei Education Department(grant no.QN2023092)High-level Talent Research Startup Project of Hebei University(grant no.521100221071,521000981426,521100223225)+17 种基金National Key R&D Plan"Nano Frontier"Key Special Project(Grant Nos.2024YFA1208400,2021YFA1200502)National Key R&D Program Disruptive Technology Innovation Project(Grant No.2024YFF1504300)National Natural Science Foundation of China(Grant Nos.62004056,62104058,Grant No.61874158)National Major R&D Project Cultivation Projects(Grant No.92164109)Natural Science Foundation of Hebei Province(Grant Nos.F2021201045,F2021201022,F2022201054,F2023201044,F2022201002)Special Support Funds for National High-Level Talents(Grant No.041500120001)Hebei Province Yanzhao Young Scientist Project(Grant No.F2023201076)Support Program for the Top Young Talents of Hebei Province(Grant No.70280011807)Hebei Province High-Level Talent Funding Project(Grant No.B20231003)Strategic Leading Science and Technology Special Project of Chinese Academy of Sciences(Grant No.XDB44000000-7)Interdisciplinary Research Program of Natural Science of Hebei University(Grant No.DXK202101)Institute of Life Sciences and Green Development(Grant No.521100311)Outstanding Young Scientific Research and Innovation Team of Hebei University(Grant No.605020521001)Advanced Talents Incubation Program of Hebei University(Grant Nos.521000981426,521100221071,521100224232,521000981363)Science and Technology Project of Hebei Education Department(Grant Nos.QN2020178,QN2021026)Baoding Science and Technology Plan Project(Grant No.2172P011)Hebei Province Key R&D Plan Projects(Grant No.22311101D)Baoding Science and Technology Plan Project(Grant No.2272P014)Regional Innovation and Development Joint Fund Key Project(Grant No.U23A20365)Hebei Province Natural Science Foundation(Grant No.F2023201044).
文摘Optical synapses have an ability to perceive and remember visual information,making them expected to provide more intelligent and efficient visual solutions for humans.As a new type of artificial visual sensory devices,photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision.However,due to the urgent problems of nonlinear conductance and high-energy consumption,its further application in high-precision control scenarios and integration is hindered.In this work,we report an optoelectronic memristor with a structure of TiN/CeO_(2)/ZnO/ITO/Mica,which can achieve minimal energy consumption(187 pJ)at a single pulse(0.5 V,5 ms).Under the stimulation of continuous pulses,linearity can be achieved up to 99.6%.In addition,the device has a variety of synaptic functions under the combined action of photoelectric,which can be used for advanced vision.By utilizing its typical long-term memory characteristics,we achieved image recognition and long-term memory in a 3×3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%.Moreover,we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform.This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems,offering a new direction for bionic eyes and visual automation technology.
基金supported by the JST SPRING Grant number JPMJSP2131funded by the Research Fellow Scheme from The Chinese University of Hong KongUniversiti Teknologi Malaysia AJ090000.6700.09453-Tabung Pembayaran Lantikan Skim Prominent Visiting Researcher Scheme JTNCPI。
文摘Recent advances in all-inorganic perovskite semiconductors have garnered significant research interest due to their potential for high-performance optoelectronic devices and enhanced stability under harsh environmental conditions.A deeper understanding of their structural,chemical,and physical properties has driven notable progress in addressing challenges related to electrical characteristics,reproducibility,and long-term operational stability in perovskite-based memristors.These advancements have been realized through composition engineering,dimensionality modulation,thin-film processing,and device optimization.This review concisely summarizes recent developments in all-inorganic perovskite memristors,highlighting their diverse material properties,device performance,and applications in artificial synapses and logic operations.We discuss key resistance-switching mechanisms,optimization strategies,and operational capabilities while outlining remaining challenges and future directions for perovskitebased memory technologies.
基金supported by the National Natural Science Foundation of China(4020969,62405044,and 52173156)Fund by Science Research Project of Hebei Education Department(HY2024050011)+1 种基金Natural Science Foundation of Sichuan Province(25NSFSC1287)Foundation of Yanshan University(1050030 and 8190299).
文摘Despite great advancements in organic mixed ionic-electronic conductors(OMIECs),their applications remain predominantly restricted to three-electrode organic electro-chemical transistors(OECTs),which rely on an additional electrolyte layer to balance ionic and electronic transport,resulting in indirect coupling of charge carriers.While direct coupling has the potential to greatly simplify device architectures,it remains underexplored in OMIECs due to the inherent imbalance between electronic and ionic conductivities.In this study,we introduce a straightforward approach to achieve balanced OMIECs and employ them as channel materials in two-electrode organic electrochemical memristors.These devices provide clear evidence of direct coupling between electronic and ionic carriers and exhibit exceptional performance in synaptic device applications.Our findings offer new insights into charge carrier transport mechanisms in OMIECs and establish organic electrochemical memristors as a promising new class of organic electronic devices for next-generation neuromorphic applications.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
基金supported by funding from the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany's Excellence Strategy-EXC 2089/1-390776260(e-conversion)via the International Research Training Group 2022 Alberta/Technical University of Munich International Graduate School for Environmentally Responsible Functional Hybrid Materials(ATUMS).
文摘With the exponential growth of the internet of things,artificial intelligence,and energy-efficient high-volume data digital communications,there is an urgent demand to develop new information technologies with high storage capacity.This needs to address the looming challenge of conventional Von Neumann architecture and Moore's law bottleneck for future data-intensive computing applications.A promising remedy lies in memristors,which offer distinct advantages of scalability,rapid access times,stable data retention,low power consumption,multistate storage capability and fast operation.Among the various materials used for active layers in memristors,low dimensional perovskite semiconductors with structural diversity and superior stability exhibit great potential for next generation memristor applications,leveraging hysteresis characteristics caused by ion migration and defects.In this review the progress of low-dimensional perovskite memory devices is comprehensively summarized.The working mechanism and fundamental processes,including ion migration dynamics,charge carrier transport and electronic resistance that underlies the switching behavior of memristors are discussed.Additionally,the device parameters are analyzed with special focus on the effective methods to improve electrical performance and operational stability.Finally,the challenges and perspective on major hurdles of low-dimensional perovskite memristors in the expansive application domains are provided.
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This work was supported by the Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017)National Research Foundation of Korea(NRF)grant funded by the Korea government(MIST)(RS-2023-00302751)+1 种基金by the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grants 2018R1A6A1A03025242 and 2018R1D1A1A09083353by Qilu Young Scholar Program of Shandong University.
文摘Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.
文摘Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.
基金supported by the National Key Research and Development Program of China (Grant No.2018YFE0203802)Natural Science Foundation of Hubei Province, China (Grant No.2022CFA031)Dongguan Innovative Research Team Program (2020607101007)。
文摘Owing to the advantages of simple structure,low power consumption and high-density integration,memristors or memristive devices are attracting increasing attention in the fields such as next generation non-volatile memories,neuromorphic computation and data encryption.However,the deposition of memristive films often requires expensive equipment,strict vacuum conditions,high energy consumption,and extended processing times.In contrast,electrochemical anodizing can produce metal oxide films quickly(e.g.10 s) under ambient conditions.By means of the anodizing technique,oxide films,oxide nanotubes,nanowires and nanodots can be fabricated to prepare memristors.Oxide film thickness,nanostructures,defect concentrations,etc,can be varied to regulate device performances by adjusting oxidation parameters such as voltage,current and time.Thus memristors fabricated by the anodic oxidation technique can achieve high device consistency,low variation,and ultrahigh yield rate.This article provides a comprehensive review of the research progress in the field of anodic oxidation assisted fabrication of memristors.Firstly,the principle of anodic oxidation is introduced;then,different types of memristors produced by anodic oxidation and their applications are presented;finally,features and challenges of anodic oxidation for memristor production are elaborated.
基金supported by the National Natural Science Foundation of China(Grant No.62061014)Technological Innovation Projects in the Field of Artificial Intelligence in Liaoning province(Grant No.2023JH26/10300011)Basic Scientific Research Projects in Department of Education of Liaoning Province(Grant No.JYTZD2023021).
文摘Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.
基金the financial support from the National Key Research and Development Program of China(Grant Nos.2018YFA0209000,2017YFB0403603)the National Natural Science Foundation of China(Grant Nos.61904173,61634006,61675191,61674050,61874158)+1 种基金the Hundred Persons Plan of Hebei Province(Grant No.E2018050004,E2018050003)the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(SLRC2019018).
文摘New neuromorphic architectures and memory technologies with low power consumption,scalability and high-speed are in the spotlight due to the von Neumann bottleneck and limitations of Moore’s law.The memristor,a two-terminal synaptic device,shows powerful capabilities in neuromorphic computing and information storage applications.Active materials with high defect migration speed and low defect migration barrier are highly promising for high-performance memristors.Halide perovskite(HP)materials with point defects(such as gaps,vacancies,and inversions)have strong application potential in memristors.In this article,we review recent advances on HP memristors with exceptional performances.First,the working mechanisms of memristors are described.Then,the structures and properties of HPs are explained.Both electrical and photonic HP-based memristors are overviewed and discussed.Different fabrication methods of HP memristor devices and arrays are described and compared.Finally,the challenges in integrating HP memristors with complementary metal oxide semiconductors(CMOS)are briefly discussed.This review can assist in developing HP memristors for the next-generation information technology.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61804079 and 61964012)the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (Grant No.KFJJ20200102)+2 种基金the Natural Science Foundation of Jiangsu Province of China (Grant Nos.BK20211273 and BZ2021031)the Nanjing University of Posts and Telecommunications (Grant No.NY220112)the Foundation of Jiangxi Science and Technology Department (Grant No.20202ACBL21200)。
文摘Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.
文摘Organic-inorganic halides perovskites(OHPs)have drawn the attention of many researchers owing to their astonishing and unique optoelectronic properties.They have been extensively used for photovoltaic applications,achieving higher than 26%power conversion efficiency to date.These materials have potential to be deployed for many other applications beyond photovoltaics like photodetectors,sensors,light-emitting diodes(LEDs),and resistors.To address the looming challenge of Moore’s law and the Von Neumann bottleneck,many new technologies regarding the computation of architectures and storage of information are being extensively researched.Since the discovery of the memristor as a fourth component of the circuit,many materials are explored for memristive applications.Lately,researchers have advanced the exploration of OHPs for memristive applications.These materials possess promising memristive properties and various kinds of halide perovskites have been used for different applications that are not only limited to data storage but expand towards artificial synapses,and neuromorphic computing.Herein we summarize the recent advancements of OHPs for memristive applications,their unique electronic properties,fabrication of materials,and current progress in this field with some future perspectives and outlooks.
基金Jiangsu Province Research Foundation(Grant Nos.BK20191202,RK106STP18003,and SZDG2018007)the Jiangsu Province Research Foundation(Grant Nos.BK20191202,RK106STP18003,and SZDG2018007)+1 种基金the Research Innovation Program for College Graduates of Jiangsu Province(Grant Nos.KYCX200806,KYCX190960,and SJCX190268)NJUPTSF(Grant Nos.NY217116,NY220078,and NY218107)。
文摘In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.
基金This work was supported in part by the Ministry of Science and Technology of China under Grant No.2017YFA0206102in part by the National Natural Science Foundation of China under Grant No.61922083+2 种基金by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDB44000000by the European Union’s Horizon 2020 Research and Innovation Program with Grant Agreement No.824164by the German Research Foundation Projects MemCrypto under Grant No.GZ:DU 1896/2-1 and MemDPU under Grant No.GZ:DU 1896/3-1.
文摘The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
基金Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(NRF-2019R1F1A1057243)together with the Future Semiconductor Device Technology Development Program(20003808,10080689,20004399)funded by MOTIE(Ministry of Trade,Industry&Energy)and KSRC(Korea Semiconductor Research Consortium).
文摘Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.
基金supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61003082) the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 60921062)
文摘As the fourth passive circuit component, a memristor is a nonlinear resistor that can ＂remember＂ the amount of charge passing through it. The characteristic of ＂remembering＂ the charge and non-volatility makes memristors great potential candidates in many fields. Nowadays, only a few groups have the ability to fabricate memristors, and most researchers study them by theoretic analysis and simulation. In this paper, we first analyse the theoretical base and characteristics of memristors, then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics. Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.
基金supported by the National Natural Science Foundation of China(61876097,61673188,61761130081)the National Key Research and Development Program of China(2016YFB0800402)+1 种基金the Foundation for Innovative Research Groups of Hubei Province of China(2017CFA005)the Fundamental Research Funds for the Central Universities(2017KFXKJC002)
文摘Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear drift.Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.
基金Project supported by the National Natural Science Foundation of China(Grant No.61674038)the Natural Science Foundation of Fujian Province,China(Grant No.2019J01218)+1 种基金the Fund from the Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China(Grant No.2021ZR145)the Science Fund from the Fujian Provincial Department of Industry and Information Technology of China(Grant No.82318075)。
文摘As an alternative device for neuromorphic computing to conquer von Neumann bottleneck,the memristor serving as an artificial synapse has attracted much attention.The TaO^(x) memristors embedded with silver nanoparticles(Ag NPs)have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs.The TaO^(x) memristors with and without Ag NPs are capable of simulating synaptic plasticity(PTP,STDP,and STP to LTP),learning,and memory behaviors.The conduction of the high resistance state(HRS) is driven by Schottky-emission mechanism.The embedment of Ag NPs causes the low resistance state(LRS) conduction governed by a Poole-Frenkel emission mechanism instead of a space-charge-limited conduction(SCLC) in a pure TaO^(x) system,which is ascribed to the Ag NPs enhancing electric field to produce additional traps and to reduce Coulomb potential energy of bound electrons to assist electron transport.Consequently,the enhanced electric fields induced by Ag NPs increase the learning strength and learning speed of the synapses.Additionally,they also improve synaptic sensitivity to stimuli.The linearity of conductance modulation and the reproducibility of conductance are improved as well.
