High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic f...High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.展开更多
The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,fle...The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.展开更多
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
As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir comput...As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.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
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
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
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
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
Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of sh...Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of short-term and long-term synaptic plasticity behaviors of the biological synapses is an es-sential step.However,the electronic synaptic devices suffer from higher variability issues which hinder the application of such devices to build neuromorphic systems.For practical applications,it is essen-tial to minimize the cycle-to-cycle and device-to-device variations in the synaptic functions of artifi-cial electronic synapses.This study involves the fabrication of diffusive memristor devices using WTe_(2) chalcogenide as the main switching material.The choice of the switching material provides a facile so-lution to the variability problem.The greater uniformity in the switching characteristics of the WTe_(2)-based memristor offers higher uniformity for the synaptic emulation.These devices exhibit both volatile and nonvolatile switching properties,allowing them to emulate both short-term and long-term synaptic functions.The WTe_(2)-based electronic synaptic devices present a high degree of uniformity for the emula-tion of various essential biological synaptic functions including short-term potentiation(STP),long-term potentiation(LTP),long-term depression(LTD),spike-rate-dependent plasticity(SRDP),and spike-timing-dependent plasticity(STDP).A higher recognition accuracy of∼92%is attained for pattern recognition using the modified National Institute of Standards and Technology(MNIST)handwritten digits,which is attributed to the enhanced linearity and higher uniformity of LTP/LTD characteristics.展开更多
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
Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilaye...Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilayer Pt/HfO_(2)/ZrO_(2-x)/HfO_(2)/TiN memristor,engineered with a ZrO_(2-x) oxygen vacancy reservoir(OVR)layer fabricated via radio frequency(RF)sputtering under controlled oxygen ambient.The incorporation of the ZrO_(2-x) OVR layer enables enhanced resistive switching characteristics,including a high ON/OFF ratio(∼8000),excellent uniformity,robust data retention(>105 s),and multilevel storage capabilities.Furthermore,the memristor demonstrates superior synaptic plasticity with linear long-term potentiation(LTP)and depression(LTD),achieving low non-linearity values of 1.36(LTP)and 0.66(LTD),and a recognition accuracy of 95.3%in an MNIST dataset simulation.The unique properties of the ZrO_(2-x) layer,particularly its ability to act as a dynamic oxygen vacancy reservoir,significantly enhance synaptic performance by stabilizing oxygen vacancy migration.These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.展开更多
In this paper, a large dynamic range floating memristor emulator(LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating voltage-controlled linear resistor(VCLR). Since...In this paper, a large dynamic range floating memristor emulator(LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating voltage-controlled linear resistor(VCLR). Since real memristors have not been largely commercialized until now, the application of a LDRFME to memristive systems is reasonable. Motivated by this need, this paper proposes an achievement of a LDRFME based on a feasible transistor model. A first circuit extends the voltage range of the triode region of an ordinary junction field effect transistor(JFET). The idea is to use this JFET transistor as a tunable linear resistor. A second memristive non-linear circuit is used to drive the resistance of the first JFET transistor. Then those two circuits are connected together and, under certain conditions, the obtained "resistor" presents a hysteretic behavior,which is considered as a memristive effect. The electrical characteristics of a LDRFME are validated by software simulation and real measurement, respectively.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
Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.12172093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607)。
文摘High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Natural Science Foundation of Jiangsu Province(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.
基金supported by the 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.
基金supported by the National R&D Pro-gram through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Nos.RS-2024-00356939 and RS-2024-00405691).
文摘As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.
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 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 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 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 Singapore Ministry of Educa-tion under Research(Grant no.MOE-T2EP50120-0003).
文摘Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realiza-tion of future computing systems.For the development of such efficient neuromorphic electronics,the emulation of short-term and long-term synaptic plasticity behaviors of the biological synapses is an es-sential step.However,the electronic synaptic devices suffer from higher variability issues which hinder the application of such devices to build neuromorphic systems.For practical applications,it is essen-tial to minimize the cycle-to-cycle and device-to-device variations in the synaptic functions of artifi-cial electronic synapses.This study involves the fabrication of diffusive memristor devices using WTe_(2) chalcogenide as the main switching material.The choice of the switching material provides a facile so-lution to the variability problem.The greater uniformity in the switching characteristics of the WTe_(2)-based memristor offers higher uniformity for the synaptic emulation.These devices exhibit both volatile and nonvolatile switching properties,allowing them to emulate both short-term and long-term synaptic functions.The WTe_(2)-based electronic synaptic devices present a high degree of uniformity for the emula-tion of various essential biological synaptic functions including short-term potentiation(STP),long-term potentiation(LTP),long-term depression(LTD),spike-rate-dependent plasticity(SRDP),and spike-timing-dependent plasticity(STDP).A higher recognition accuracy of∼92%is attained for pattern recognition using the modified National Institute of Standards and Technology(MNIST)handwritten digits,which is attributed to the enhanced linearity and higher uniformity of LTP/LTD characteristics.
基金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financially supported by the National Research Foundation of Korea(no.NRF-2021R1A2C2010781)grant funded by the Korean Government(Ministry of Science and ICT)Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(no.P0012451,The Competency Development Program for Industry Specialist)Korea Government(MOTIE)(no.P0020966,HRD Program for Industrial Innovation).
文摘Neuromorphic computing devices leveraging HfO_(2) and ZrO_(2) materials have recently garnered significant attention due to their potential for brain-inspired computing systems.In this study,we present a novel trilayer Pt/HfO_(2)/ZrO_(2-x)/HfO_(2)/TiN memristor,engineered with a ZrO_(2-x) oxygen vacancy reservoir(OVR)layer fabricated via radio frequency(RF)sputtering under controlled oxygen ambient.The incorporation of the ZrO_(2-x) OVR layer enables enhanced resistive switching characteristics,including a high ON/OFF ratio(∼8000),excellent uniformity,robust data retention(>105 s),and multilevel storage capabilities.Furthermore,the memristor demonstrates superior synaptic plasticity with linear long-term potentiation(LTP)and depression(LTD),achieving low non-linearity values of 1.36(LTP)and 0.66(LTD),and a recognition accuracy of 95.3%in an MNIST dataset simulation.The unique properties of the ZrO_(2-x) layer,particularly its ability to act as a dynamic oxygen vacancy reservoir,significantly enhance synaptic performance by stabilizing oxygen vacancy migration.These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.
基金supported by the National Key Research and Development Program of China(2018YFC0830300)the National Natural Science Foundation of China(61571312)the Science and Technology Support Project of Chengdu PU Chip Science and Technology Co.,Ltd
文摘In this paper, a large dynamic range floating memristor emulator(LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating voltage-controlled linear resistor(VCLR). Since real memristors have not been largely commercialized until now, the application of a LDRFME to memristive systems is reasonable. Motivated by this need, this paper proposes an achievement of a LDRFME based on a feasible transistor model. A first circuit extends the voltage range of the triode region of an ordinary junction field effect transistor(JFET). The idea is to use this JFET transistor as a tunable linear resistor. A second memristive non-linear circuit is used to drive the resistance of the first JFET transistor. Then those two circuits are connected together and, under certain conditions, the obtained "resistor" presents a hysteretic behavior,which is considered as a memristive effect. The electrical characteristics of a LDRFME are validated by software simulation and real measurement, respectively.
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 Research Fund for International Young Scientists of the National Natural Science Foundation of China(61550110248)the Research on Fundamental Theory of Shared Intelligent Street Lamp for New Scene Service(H04W200495)+1 种基金Sichuan Science and Technology Program(2019YFG0190)the Research on Sino-Tibetan Multi-source Information Acquisition,Fusion,Data Mining and its Application(H04W170186).
文摘Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
