Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app...Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.展开更多
D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers u...D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R1 and R2.R1(RuⅡ-(tpz)Cl2)features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R2(RuⅡ-(bpp)2)incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R1 facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R2 metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R1 exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru+2/+3redox potential relative to R2.Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R1.Additionally,R2 metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.展开更多
This study investigates the dynamics of discrete memristive FitzHugh–Nagumo(FHN)neural networks.We introduce a discrete memristor with hyperbolic tangent nonlinearity and incorporate it into neuron models ranging fro...This study investigates the dynamics of discrete memristive FitzHugh–Nagumo(FHN)neural networks.We introduce a discrete memristor with hyperbolic tangent nonlinearity and incorporate it into neuron models ranging from single neurons and coupled pairs to complex networks with ring and small-world topologies.Stability and bifurcation analyses reveal transitions from periodic to chaotic dynamics.A key contribution is the identification of a constant fixed point that remains invariant across periodic,weakly chaotic,and chaotic regimes.Linear stability analysis of this fixed point provides a fundamental basis for understanding the system's dynamical evolution.The fixed point theory explains how memristive coupling induces diverse synchronization patterns,including stable phase-locking and synchronization–desynchronization transitions,and further accounts for the emergence of chimera states in ring networks as well as their alteration in smallworld networks owing to long-range connections.Field-programmable gate array(FPGA)implementation successfully validates the mathematical models,confirming the feasibility of hardware realization.Overall,this work establishes a theoretical framework linking fixed point properties with firing mechanisms and synchronization dynamics in discrete memristive FHN neural networks,providing insights into potential applications in neuromorphic computing.展开更多
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica...Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness.展开更多
Memristor-based neural networks are one of the most promising approaches for the hardware implementation of artificial neural networks.In this paper,a memristor-based neural network circuit based on a one-memristor–o...Memristor-based neural networks are one of the most promising approaches for the hardware implementation of artificial neural networks.In this paper,a memristor-based neural network circuit based on a one-memristor–one-resistor(1M1R)synaptic array structure is designed for character recognition.Compared with other memristive synaptic arrays,the 1M1R structure can reduce the number of memristors used.However,memristors may malfunction due to fabrication defects and the influence of external factors,resulting in a decrease in the accuracy of the circuit's character recognition,and a suitable solution needs to be found to improve the stability and durability of the circuit.Therefore,in this paper,a fault-tolerant module with feedback adjustment capability is designed in the memristive neural network circuit that can readjust the weights of the memristors through in-situ training to solve multiple faults in the memristive neural network.The effect of fault tolerance is verified by character recognition.The experimental results show that the designed memristive neural network circuit can accurately realize character recognition,and the designed fault-tolerant circuit can well tolerate multiple faults,ensuring stable operation of the circuit under fault conditions.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
随着电子工业的快速发展,电子元器件的生产与使用规模持续扩大,电子元器件的需求量也逐年递增,传统的手工计数方法在实际操作中极易受到操作人员疲劳、主观判断差异等人为因素的影响,导致元器件识别效率低下且计数精度难以保证,已无法...随着电子工业的快速发展,电子元器件的生产与使用规模持续扩大,电子元器件的需求量也逐年递增,传统的手工计数方法在实际操作中极易受到操作人员疲劳、主观判断差异等人为因素的影响,导致元器件识别效率低下且计数精度难以保证,已无法满足现代工业生产对高效率、高精度的迫切需求。为解决上述问题,设计并实现了一套基于机器视觉技术的电子元器件自动识别与计数系统。该系统以Halcon机器视觉软件平台为核心,充分利用其丰富的图像处理与模式识别算法库,通过算法筛选与优化,选取适用于元器件特征提取与定位的核心视觉算子,并将其无缝集成至基于Visual Studio C#开发环境所构建的上位机应用程序中。该系统实现了对电阻、电容等常见分立元器件的分类与计数功能,具备识别准确率高、计数稳定性好等特点。试验结果表明,该系统能显著提升元器件识别的自动化水平与计数精度,有效克服了人工操作带来的不确定性。展开更多
Constructing a neuromorphic electromechanical system based on a flexible memristor is of great significance for the development of biomimetic electrical-mechanical transverters.A bioinspired electromechanical system c...Constructing a neuromorphic electromechanical system based on a flexible memristor is of great significance for the development of biomimetic electrical-mechanical transverters.A bioinspired electromechanical system can perform real-time energy-efficient processing of multimodal signals,including electrical activities and mechanical motions.Here,we propose a bioinspired electromechanical system composed of a two-disc dynamo driven by a dual integrate-and-fire neuron.A memristive system exists in the bioinspired electromechanical system,as observed in the current-voltage relationship.The neuromorphic electromechanical model can exhibit a multiscroll hidden attractor by adjusting a controllable parameter.Complex chaotic behaviors have been demonstrated by numerical simulations,including two-parameter bifurcation,Lyapunov exponents,and phase diagrams.Finally,the applicability of a chaotic encryption scheme is successfully implemented on a neuromorphic electromechanical system.展开更多
基金supported by the National Key R&D Project from the Minister of Science and Technology(2024YFA1211500)the National Natural Science Foundation of China(Grant Nos.62304130,62405158 and 62574123)+1 种基金the Shanghai youth science and technology star project(24QA2702800)Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle。
摘要Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.
摘要D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R1 and R2.R1(RuⅡ-(tpz)Cl2)features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R2(RuⅡ-(bpp)2)incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R1 facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R2 metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R1 exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru+2/+3redox potential relative to R2.Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R1.Additionally,R2 metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.
基金supported by the Natural Science Foundation of China(Grant Nos.62501516,61901530,62071496,62061008)the Natural Science Foundation of Hunan Province(Grant No.2020JJ5767)the Natural Science Foundation of Hunan Province(Grant No.2025JJ50391)。
摘要This study investigates the dynamics of discrete memristive FitzHugh–Nagumo(FHN)neural networks.We introduce a discrete memristor with hyperbolic tangent nonlinearity and incorporate it into neuron models ranging from single neurons and coupled pairs to complex networks with ring and small-world topologies.Stability and bifurcation analyses reveal transitions from periodic to chaotic dynamics.A key contribution is the identification of a constant fixed point that remains invariant across periodic,weakly chaotic,and chaotic regimes.Linear stability analysis of this fixed point provides a fundamental basis for understanding the system's dynamical evolution.The fixed point theory explains how memristive coupling induces diverse synchronization patterns,including stable phase-locking and synchronization–desynchronization transitions,and further accounts for the emergence of chimera states in ring networks as well as their alteration in smallworld networks owing to long-range connections.Field-programmable gate array(FPGA)implementation successfully validates the mathematical models,confirming the feasibility of hardware realization.Overall,this work establishes a theoretical framework linking fixed point properties with firing mechanisms and synchronization dynamics in discrete memristive FHN neural networks,providing insights into potential applications in neuromorphic computing.
基金supported by the National Natural Science Foundation of China(Grant Nos.62501516 and 62572419)the Natural Science Foundation of Hunan Province(Grant Nos.2025JJ50391 and 2025JJ50392)the Research Foundation of the Education Department of Hunan Province(Grant Nos.23B0131 and 24A0124)。
摘要Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness.
基金supported by the Natural Science Foundation of Shandong Province(Grant No.ZR2022MF225)the National Natural Science Foundation of China(Grant Nos.62176143 and 62371275)。
摘要Memristor-based neural networks are one of the most promising approaches for the hardware implementation of artificial neural networks.In this paper,a memristor-based neural network circuit based on a one-memristor–one-resistor(1M1R)synaptic array structure is designed for character recognition.Compared with other memristive synaptic arrays,the 1M1R structure can reduce the number of memristors used.However,memristors may malfunction due to fabrication defects and the influence of external factors,resulting in a decrease in the accuracy of the circuit's character recognition,and a suitable solution needs to be found to improve the stability and durability of the circuit.Therefore,in this paper,a fault-tolerant module with feedback adjustment capability is designed in the memristive neural network circuit that can readjust the weights of the memristors through in-situ training to solve multiple faults in the memristive neural network.The effect of fault tolerance is verified by character recognition.The experimental results show that the designed memristive neural network circuit can accurately realize character recognition,and the designed fault-tolerant circuit can well tolerate multiple faults,ensuring stable operation of the circuit under fault conditions.
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.
摘要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.
摘要随着电子工业的快速发展,电子元器件的生产与使用规模持续扩大,电子元器件的需求量也逐年递增,传统的手工计数方法在实际操作中极易受到操作人员疲劳、主观判断差异等人为因素的影响,导致元器件识别效率低下且计数精度难以保证,已无法满足现代工业生产对高效率、高精度的迫切需求。为解决上述问题,设计并实现了一套基于机器视觉技术的电子元器件自动识别与计数系统。该系统以Halcon机器视觉软件平台为核心,充分利用其丰富的图像处理与模式识别算法库,通过算法筛选与优化,选取适用于元器件特征提取与定位的核心视觉算子,并将其无缝集成至基于Visual Studio C#开发环境所构建的上位机应用程序中。该系统实现了对电阻、电容等常见分立元器件的分类与计数功能,具备识别准确率高、计数稳定性好等特点。试验结果表明,该系统能显著提升元器件识别的自动化水平与计数精度,有效克服了人工操作带来的不确定性。
基金supported by the Ningxia Natural Science F oundation Project(Nos.2024 AAC01001 and 2024 AAC 05002)the National Natural Science Foundation of China(No.12302070)the Youth Science and Technology Talent Cultivation Project of Ningxia Hui Autonomous Region,China。
摘要Constructing a neuromorphic electromechanical system based on a flexible memristor is of great significance for the development of biomimetic electrical-mechanical transverters.A bioinspired electromechanical system can perform real-time energy-efficient processing of multimodal signals,including electrical activities and mechanical motions.Here,we propose a bioinspired electromechanical system composed of a two-disc dynamo driven by a dual integrate-and-fire neuron.A memristive system exists in the bioinspired electromechanical system,as observed in the current-voltage relationship.The neuromorphic electromechanical model can exhibit a multiscroll hidden attractor by adjusting a controllable parameter.Complex chaotic behaviors have been demonstrated by numerical simulations,including two-parameter bifurcation,Lyapunov exponents,and phase diagrams.Finally,the applicability of a chaotic encryption scheme is successfully implemented on a neuromorphic electromechanical system.