The deceleration of Moore's law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond-CMOS designs that integrate memory and compute.Self-rectifying memristors(SRMs...The deceleration of Moore's law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond-CMOS designs that integrate memory and compute.Self-rectifying memristors(SRMs)have emerged as promising building blocks for high-performance,low-power systems by combining resistive switching with intrinsic diode-like behavior.Their unidirectional conduction inhibits sneak-path currents in crossbar arrays devoid of external selectors,while nonlinear I–V characteristics,adjustable conductance states,low operating voltages,and rapid switching facilitate efficient vector–matrix operations,neuromorphic plasticity,and hardware security primitives.This review synthesizes the working mechanisms of SRMs,surveys material,and structural strategies and compares device metrics relevant to array-scale deployment(rectification ratio,nonlinearity,endurance,retention,variability,and operating voltage).We assess SRM-enabled in-memory computing and neuromorphic applications,as well as security functions such as physical unclonable functions and reconfigurable cryptographic primitives.Integration pathways toward CMOS compatibility are analyzed,including back-end-of-line thermal budgets,uniformity,write disturb mitigation,and reliability.Finally,we outline key challenges and opportunities:materials/architecture co-design,precision analog training,stochasticity control/exploitation,3D stacking,and standardized benchmarking that can accelerate large-scale SRM adoption.Through the use of specialized materials and structural optimization,SRMs are set to provide selector-free,densely integrated,and energy-efficient hardware for future information processing.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 R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) 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
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
The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,fle...The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.展开更多
Neuromorphic circuits based on superconducting tunnel junctions have attracted much attention due to their highspeed computing capabilities and low energy consumption.Josephson junction circuits can effectively mimic ...Neuromorphic circuits based on superconducting tunnel junctions have attracted much attention due to their highspeed computing capabilities and low energy consumption.Josephson junction circuits can effectively mimic biological neural dynamics.Leveraging these advantages,we construct a Josephson junction neuron-like model with a phasedependent dissipative current,referred to as a memristive current.The proposed memristive Josephson junction model exhibits complex dynamical behaviors.Furthermore,considering the effect of a fast-modulated synapse,we explore synchronization phenomena in coupled networks under varying coupling conductances and excitatory/inhibitory interactions.Finally,we extend the neuromorphic Josephson junction model—exhibiting complex dynamics—to the field of image encryption.These results not only enrich the understanding of the dynamical characteristics of memristive Josephson junctions but also provide a theoretical basis and technical support for the development of new neural networks and their applications in information security technology.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.92364204 and 62204219)the open research fund of Suzhou Laboratory(Grants No.SZLAB-1208-2024-TS012)+1 种基金Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401)Zhejiang Province Introduces and Cultivates Leading Innovation and Entrepreneurship Teams(Grants No.2023R01011)。
文摘The deceleration of Moore's law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond-CMOS designs that integrate memory and compute.Self-rectifying memristors(SRMs)have emerged as promising building blocks for high-performance,low-power systems by combining resistive switching with intrinsic diode-like behavior.Their unidirectional conduction inhibits sneak-path currents in crossbar arrays devoid of external selectors,while nonlinear I–V characteristics,adjustable conductance states,low operating voltages,and rapid switching facilitate efficient vector–matrix operations,neuromorphic plasticity,and hardware security primitives.This review synthesizes the working mechanisms of SRMs,surveys material,and structural strategies and compares device metrics relevant to array-scale deployment(rectification ratio,nonlinearity,endurance,retention,variability,and operating voltage).We assess SRM-enabled in-memory computing and neuromorphic applications,as well as security functions such as physical unclonable functions and reconfigurable cryptographic primitives.Integration pathways toward CMOS compatibility are analyzed,including back-end-of-line thermal budgets,uniformity,write disturb mitigation,and reliability.Finally,we outline key challenges and opportunities:materials/architecture co-design,precision analog training,stochasticity control/exploitation,3D stacking,and standardized benchmarking that can accelerate large-scale SRM adoption.Through the use of specialized materials and structural optimization,SRMs are set to provide selector-free,densely integrated,and energy-efficient hardware for future information processing.
文摘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 R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) 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 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。
文摘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.
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(Grant No.12302070)the Natural Science Foundation of Ningxia(Grant No.2024AAC05002)+1 种基金the Youth Science and Technology Talent Cultivation Project of Ningxiathe Ningxia Science and Technology Leading Talent Training Program(Grant No.2022GKLRLX04)。
文摘Neuromorphic circuits based on superconducting tunnel junctions have attracted much attention due to their highspeed computing capabilities and low energy consumption.Josephson junction circuits can effectively mimic biological neural dynamics.Leveraging these advantages,we construct a Josephson junction neuron-like model with a phasedependent dissipative current,referred to as a memristive current.The proposed memristive Josephson junction model exhibits complex dynamical behaviors.Furthermore,considering the effect of a fast-modulated synapse,we explore synchronization phenomena in coupled networks under varying coupling conductances and excitatory/inhibitory interactions.Finally,we extend the neuromorphic Josephson junction model—exhibiting complex dynamics—to the field of image encryption.These results not only enrich the understanding of the dynamical characteristics of memristive Josephson junctions but also provide a theoretical basis and technical support for the development of new neural networks and their applications in information security technology.
