Photoresponsive memristors(i.e.,photomemristors)have been recently highly regarded to tackle data latency and energy consumption challenges in conventional Von Neumann architecture-based image recognition systems.Howe...Photoresponsive memristors(i.e.,photomemristors)have been recently highly regarded to tackle data latency and energy consumption challenges in conventional Von Neumann architecture-based image recognition systems.However,their efficacy in recognizing low-contrast images is quite limited,and while preprocessing algorithms are usually employed to enhance these images,which naturally introduce delays that hinder real-time recognition in complex conditions.To address this challenge,here we present a selfdriven polarization-sensitive ferroelectric photomemristor inspired by advanced biological systems.The proposed prototype device is engineered to extract image polarization information,enabling real-time and in-situ enhanced image recognition and classification capabilities.By combining the anisotropic optical feature of the two-dimensional material(ReSe_(2))and ferroelectric polarization of singlecrystalline diisopropylammonium bromide(DIPAB)thin film,tunable and self-driven polarized responsiveness with intelligence was achieved.With remarkable optoelectronic synaptic characteristics of the fabricated device,a significant enhancement was demonstrated in recognition probability—averaging an impressive 85.9% for low-contrast scenarios,in contrast to the mere 47.5% exhibited by traditional photomemristors.This holds substantial implications for the detection and recognition of subtle information in diverse scenes such as autonomous driving,medical imaging,and astronomical observation.展开更多
This work presents a high-stability self-rectifying memristor(SRM)array based on the Pt/TaO_(x)/Ti structure,with an indepth investigation of the performance and potential applications of the device.The device demonst...This work presents a high-stability self-rectifying memristor(SRM)array based on the Pt/TaO_(x)/Ti structure,with an indepth investigation of the performance and potential applications of the device.The device demonstrates excellent rectification and on/off ratios,along with low-power readout,multi-state storage,and multi-level switching capabilities,highlighting its practicality and adaptability.Notably,the device exhibits outstanding fluctuation suppression and exceptional uniformity.The coefficient of variation(CV)of the rectification ratio,calculated as 0.11497 at 3 V,indicates its high stability under multiple cycles and low-voltage operation,making it well-suited for large-scale integration and operational applications.Moreover,the stability of the rectification ratio further reinforces its potential as a hardware foundation for large-scale inmemory computing systems.By combining the neuromorphic characteristics of the device with a simulated annealing algorithm and optimizing the annealing temperature function,the system emulates biological neuron behavior,enabling fast and efficient image restoration tasks.Experimental results demonstrate that this approach significantly outperforms traditional algorithms in both optimization speed and repair accuracy.The present study offers a novel perspective for the design of in-memory computing hardware and showcases promising applications in neuromorphic computing and image processing.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
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
基金supported by the National Key Research and Development Program of China for International Cooperation under Grant 2023YFE0117100the National Natural Science Foundation of China(Nos.62074040 and 62074045).
文摘Photoresponsive memristors(i.e.,photomemristors)have been recently highly regarded to tackle data latency and energy consumption challenges in conventional Von Neumann architecture-based image recognition systems.However,their efficacy in recognizing low-contrast images is quite limited,and while preprocessing algorithms are usually employed to enhance these images,which naturally introduce delays that hinder real-time recognition in complex conditions.To address this challenge,here we present a selfdriven polarization-sensitive ferroelectric photomemristor inspired by advanced biological systems.The proposed prototype device is engineered to extract image polarization information,enabling real-time and in-situ enhanced image recognition and classification capabilities.By combining the anisotropic optical feature of the two-dimensional material(ReSe_(2))and ferroelectric polarization of singlecrystalline diisopropylammonium bromide(DIPAB)thin film,tunable and self-driven polarized responsiveness with intelligence was achieved.With remarkable optoelectronic synaptic characteristics of the fabricated device,a significant enhancement was demonstrated in recognition probability—averaging an impressive 85.9% for low-contrast scenarios,in contrast to the mere 47.5% exhibited by traditional photomemristors.This holds substantial implications for the detection and recognition of subtle information in diverse scenes such as autonomous driving,medical imaging,and astronomical observation.
基金the National Natural Science Foundation of China(No.U23A20322)the National Key Research and Development Program of China(Nos.2023YFF0719600,2021YFA1202600,and 2021YFB4000800)+4 种基金the CAS Project for Young Scientists in Basic Research(No.YSBR-113)the Ningbo Technology Project(No.2022A-007-C)the Hunan Provincial Natural Science Foundation(Nos.2023JJ50009,2025JJ60351,and 2023JJ30599)the Foundation of Innovation Center of Radiation Application(No.KFZC2023020701)the Major Scientific and Technological Innovation Platform Project of Hunan Province(No.2024JC1003).
文摘This work presents a high-stability self-rectifying memristor(SRM)array based on the Pt/TaO_(x)/Ti structure,with an indepth investigation of the performance and potential applications of the device.The device demonstrates excellent rectification and on/off ratios,along with low-power readout,multi-state storage,and multi-level switching capabilities,highlighting its practicality and adaptability.Notably,the device exhibits outstanding fluctuation suppression and exceptional uniformity.The coefficient of variation(CV)of the rectification ratio,calculated as 0.11497 at 3 V,indicates its high stability under multiple cycles and low-voltage operation,making it well-suited for large-scale integration and operational applications.Moreover,the stability of the rectification ratio further reinforces its potential as a hardware foundation for large-scale inmemory computing systems.By combining the neuromorphic characteristics of the device with a simulated annealing algorithm and optimizing the annealing temperature function,the system emulates biological neuron behavior,enabling fast and efficient image restoration tasks.Experimental results demonstrate that this approach significantly outperforms traditional algorithms in both optimization speed and repair accuracy.The present study offers a novel perspective for the design of in-memory computing hardware and showcases promising applications in neuromorphic computing and image processing.
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.
