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
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ...Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.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
Memristor chaotic research has become a hotspot in the academic world.However,there is little exploration combining memristor and stochastic resonance,and the correlation research between chaos and stochastic resonanc...Memristor chaotic research has become a hotspot in the academic world.However,there is little exploration combining memristor and stochastic resonance,and the correlation research between chaos and stochastic resonance is still in the preliminary stage.In this paper,we focus on the stochastic resonance induced by memristor chaos,which enhances the dynamics of chaotic systems through the introduction of memristor and induces memristor stochastic resonance under certain conditions.First,the memristor chaos model is constructed,and the memristor stochastic resonance model is constructed by adjusting the parameters of the memristor chaos model.Second,the combination of dynamic analysis and experimental verification is used to analyze the memristor stochastic resonance and to investigate the trend of the output signal of the system under different amplitudes of the input signal.Finally,the practicality and reliability of the constructed model are further verified through the design and testing of the analog circuit,which provides strong support for the practical application of the memristor chaos-induced stochastic resonance model.展开更多
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
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
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
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
Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantag...Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantages such as nonvolatility,high density,low power consumption,and high response ratio,memristors are regarded as devices with promising applications in brain-inspired intelligence.This paper proposes a physical Ag/HfO_(x)/FeO_(x)/Pt memristor model.The Ag/HfO_(x)/FeO_(x)/Pt memristor is first fabricated using magnetron sputtering,and its internal principles and characteristics are then thoroughly analyzed.Furthermore,we construct a corresponding physical memristor model which achieves a simulation accuracy of up to 99.72%for the physical memristor.We design a fully functional Pavlovian associative memory circuit,realizing functions including generalization,primary differentiation,secondary differentiation,and forgetting.Finally,the circuit is validated through PSPICE simulation and analysis.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
A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the expl...A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the explaination of the underlying mechanism for these fingerprints is still limited.In this paper,we propose the differential form of the memristor function,and we disclose the dynamical mechanism of the memristor according to the differential form.The symmetry of the curve is only determined by the driven signal,and the shrinking loop area results from the shrinking area enclosed by driven signal and the time coordinate axis.Significantly,we find the condition for the phase transition of a memristor,and the resistance switches between the positive resistance,local zero resistance,and local negative resistance.This phase transition is confirmed in the HP memristor.These results advance the understanding of the dynamics mechanism and phase transition of a memristor.展开更多
Since the method of discretizing memristors was proposed,discrete memristors(DMs)have become a very important topic in recent years.However,there has been little research on non-autonomous discrete memristors(NDMs)and...Since the method of discretizing memristors was proposed,discrete memristors(DMs)have become a very important topic in recent years.However,there has been little research on non-autonomous discrete memristors(NDMs)and their applications.Therefore,in this paper,a new NDM is constructed,and a non-autonomous hyperchaotic map is proposed by connecting this non-autonomous memristor in parallel with an autonomous memristor.This map exhibits complex dynamical behaviors,including infinitely many fixed points,initial-boosted attractors,initial-boosted bifurcations,and the size of the attractors being controlled by the initial value.In addition,a simple pseudo-random number generator(PRNG)was designed using the non-autonomous hyperchaotic map,and the pseudo-random numbers(PRNs)generated by it were tested using the National Institute of Standards and Technology(NIST)SP800-22 test suite.Finally,the non-autonomous hyperchaotic map is implemented on the STM32 hardware experimental platform.展开更多
Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural networks.As a type of neural network whose dynamic behavior can be explained,the coupling of resonant ...Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural networks.As a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the literature.Therefore,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the RTD-MCNN.Based on this model,a simple encryption scheme for the protection of digital images in police forensic applications is proposed.The results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting multistability.Furthermore,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear variations.Undoubtedly,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection.Encryption tests and security analyses validate the effectiveness of this scheme.展开更多
Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and l...Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.展开更多
The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising altern...The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.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
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
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
As a new type of nonvolatile memory,the resistive memristor has broad application prospects in in-formation storage and neural computing based on its excellent resistive switching(RS)performance.At present,it is still...As a new type of nonvolatile memory,the resistive memristor has broad application prospects in in-formation storage and neural computing based on its excellent resistive switching(RS)performance.At present,it is still a great challenge to improve both ferroelectric polarization and leakage current to achieve a high RS on/offratio of ferroelectric memristors.Herein,epitaxial Pb(Zr_(0.40)Ti_(0.60))O_(3)(PZT)thin films with low content Ca doping were deposited on the Nb:SrTiO_(3)substrate to prepare PCZT/NSTO het-erostructures and their RS behaviors were studied.The research findings show that compared with pure PZT film,the ferroelectric polarization of 1-mol%-Ca-doped PZT film is slightly improved,while the leak-age current is increased by three orders of magnitude.Therefore,the RS on/offratio reaches 2.5×10^(5),about three orders of magnitude higher than pure PZT films.The theoretical analysis reveals that the RS behavior of PCZT/NSTO heterostructures is controlled by the PCZT/NSTO interfacial barrier and the space charge-limited current mechanism.Our results demonstrate that the ferroelectricity and electricity of ferroelectric thin films can be improved simultaneously by doping low-content Ca ions to increase the RS performance,which provides a good reference for the development of high-performance ferroelectric memristor devices.展开更多
The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating...The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.展开更多
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supported by the"Science and Technology Development Plan Project of Jilin Province,China"(Grant No.20240101018JJ)the Fundamental Research Funds for the Central Universities(Grant No.2412023YQ004)the National Natural Science Foundation of China(Grant Nos.52072065,52272140,52372137,and U23A20568).
文摘Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.
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.
文摘Memristor chaotic research has become a hotspot in the academic world.However,there is little exploration combining memristor and stochastic resonance,and the correlation research between chaos and stochastic resonance is still in the preliminary stage.In this paper,we focus on the stochastic resonance induced by memristor chaos,which enhances the dynamics of chaotic systems through the introduction of memristor and induces memristor stochastic resonance under certain conditions.First,the memristor chaos model is constructed,and the memristor stochastic resonance model is constructed by adjusting the parameters of the memristor chaos model.Second,the combination of dynamic analysis and experimental verification is used to analyze the memristor stochastic resonance and to investigate the trend of the output signal of the system under different amplitudes of the input signal.Finally,the practicality and reliability of the constructed model are further verified through the design and testing of the analog circuit,which provides strong support for the practical application of the memristor chaos-induced stochastic resonance model.
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 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 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 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Project supported by the National Natural Science Foundation of China(Grant Nos.62476230 and 61976246)the Natural Science Foundation of Chongqing(Grant No.CSTB2023NSCQ-MSX0018)Fundamental Research Funds for the Central Universities(Grant No.SWUKR22046)。
文摘Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantages such as nonvolatility,high density,low power consumption,and high response ratio,memristors are regarded as devices with promising applications in brain-inspired intelligence.This paper proposes a physical Ag/HfO_(x)/FeO_(x)/Pt memristor model.The Ag/HfO_(x)/FeO_(x)/Pt memristor is first fabricated using magnetron sputtering,and its internal principles and characteristics are then thoroughly analyzed.Furthermore,we construct a corresponding physical memristor model which achieves a simulation accuracy of up to 99.72%for the physical memristor.We design a fully functional Pavlovian associative memory circuit,realizing functions including generalization,primary differentiation,secondary differentiation,and forgetting.Finally,the circuit is validated through PSPICE simulation and analysis.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Natural Science Foundation of Jiangsu Province(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.
基金supported by the National Natural Science Foundation of China under Grant Nos.62071496,62061008the Research and Innovation Project of Graduate of Central South University under Grant No.2023ZZTS0168.
文摘A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the explaination of the underlying mechanism for these fingerprints is still limited.In this paper,we propose the differential form of the memristor function,and we disclose the dynamical mechanism of the memristor according to the differential form.The symmetry of the curve is only determined by the driven signal,and the shrinking loop area results from the shrinking area enclosed by driven signal and the time coordinate axis.Significantly,we find the condition for the phase transition of a memristor,and the resistance switches between the positive resistance,local zero resistance,and local negative resistance.This phase transition is confirmed in the HP memristor.These results advance the understanding of the dynamics mechanism and phase transition of a memristor.
基金supported by the National Natural Science Foundation of China(Grant No.62071411).
文摘Since the method of discretizing memristors was proposed,discrete memristors(DMs)have become a very important topic in recent years.However,there has been little research on non-autonomous discrete memristors(NDMs)and their applications.Therefore,in this paper,a new NDM is constructed,and a non-autonomous hyperchaotic map is proposed by connecting this non-autonomous memristor in parallel with an autonomous memristor.This map exhibits complex dynamical behaviors,including infinitely many fixed points,initial-boosted attractors,initial-boosted bifurcations,and the size of the attractors being controlled by the initial value.In addition,a simple pseudo-random number generator(PRNG)was designed using the non-autonomous hyperchaotic map,and the pseudo-random numbers(PRNs)generated by it were tested using the National Institute of Standards and Technology(NIST)SP800-22 test suite.Finally,the non-autonomous hyperchaotic map is implemented on the STM32 hardware experimental platform.
基金supported by the Scientific Research Fund of Hunan Provincial Education Department(Grant No.24A0248)the National Key Research and Development Program“National Quality Infrastructure System”Special Project(Grant No.2024YFF0617900)the Hefei Minglong Electronic Technology Co.,Ltd.(Grant Nos.2024ZKHX293,2024ZKHX294,and 2024ZKHX295).
文摘Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural networks.As a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the literature.Therefore,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the RTD-MCNN.Based on this model,a simple encryption scheme for the protection of digital images in police forensic applications is proposed.The results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting multistability.Furthermore,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear variations.Undoubtedly,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection.Encryption tests and security analyses validate the effectiveness of this scheme.
基金supported financially by the fund from the Ministry of Science and Technology of China(Grant No.2019YFB2205100)the National Science Fund for Distinguished Young Scholars(No.52025022)+3 种基金the National Nature Science Foundation of China(Grant Nos.U19A2091,62004016,51732003,52072065,1197407252272140 and 52372137)the‘111’Project(Grant No.B13013)the Fundamental Research Funds for the Central Universities(Nos.2412023YQ004 and 2412022QD036)the funding from Jilin Province(Grant Nos.20210201062GX,20220502002GH,20230402072GH,20230101017JC and 20210509045RQ)。
文摘Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.
基金This work was supported by the National Research Foundation,Singapore under Award No.NRF-CRP24-2020-0002.
文摘The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.
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.
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the Central Government Guiding Local Science and Technology Development Funds of Liaoning Province in 2021(No.2021JH6/10500168)the National Basic Research Program(No.2017YFA0206302)of Chinafor theirsupport tothis work.
文摘As a new type of nonvolatile memory,the resistive memristor has broad application prospects in in-formation storage and neural computing based on its excellent resistive switching(RS)performance.At present,it is still a great challenge to improve both ferroelectric polarization and leakage current to achieve a high RS on/offratio of ferroelectric memristors.Herein,epitaxial Pb(Zr_(0.40)Ti_(0.60))O_(3)(PZT)thin films with low content Ca doping were deposited on the Nb:SrTiO_(3)substrate to prepare PCZT/NSTO het-erostructures and their RS behaviors were studied.The research findings show that compared with pure PZT film,the ferroelectric polarization of 1-mol%-Ca-doped PZT film is slightly improved,while the leak-age current is increased by three orders of magnitude.Therefore,the RS on/offratio reaches 2.5×10^(5),about three orders of magnitude higher than pure PZT films.The theoretical analysis reveals that the RS behavior of PCZT/NSTO heterostructures is controlled by the PCZT/NSTO interfacial barrier and the space charge-limited current mechanism.Our results demonstrate that the ferroelectricity and electricity of ferroelectric thin films can be improved simultaneously by doping low-content Ca ions to increase the RS performance,which provides a good reference for the development of high-performance ferroelectric memristor devices.
基金Project supported by the Key Projects of Hunan Provincial Department of Education (Grant No.23A0133)the Natural Science Foundation of Hunan Province (Grant No.2022JJ30572)the National Natural Science Foundations of China (Grant No.62171401)。
文摘The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.
