To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the vario...To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.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
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
Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural n...Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.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
The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language mod...The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.展开更多
Photoelectric synaptic devices could emulate synaptic behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation and low crosstalk. In this study, we introduced a novel InG...Photoelectric synaptic devices could emulate synaptic behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation and low crosstalk. In this study, we introduced a novel InGaZnO-based photoelectric memristor. Under both electrical and optical stimulation, the device successfully emulated synaptic characteristics including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD). Furthermore, we demonstrated the practical application of our synaptic devices through the recognition of handwritten digits. The devices have successfully shown their ability to modulate synaptic weights effectively through light pulse stimulation, resulting in a recognition accuracy of up to 93.4%. The results illustrated the potential of IGZO-based memristors in neuromorphic computing, particularly their ability to simulate synaptic functionalities and contribute to image recognition tasks.展开更多
Artificial synapses are essential building blocks for neuromorphic electronics.Here,solid polymer electrolyte-gated artificial synapses(EGASs)were fabricated using ITO fibers as channels,which possess an ultra-high se...Artificial synapses are essential building blocks for neuromorphic electronics.Here,solid polymer electrolyte-gated artificial synapses(EGASs)were fabricated using ITO fibers as channels,which possess an ultra-high sensitivity of 5 m V and a long-term memory time exceeding 3 min.Notably,digitally printed ITO-fiber arrays exhibit an ultra-high transmittance of approximately 99.67%.Biological synaptic plasticity,such as excitatory postsynaptic current,paired-pulse facilitation,spike frequency-dependent plasticity,and synaptic potentiation and depression,were successfully mimicked using the EGASs.Based on the synaptic properties of the EGASs,an artificial neural network was constructed to perform supervised learning using the Fashion-MNIST dataset,achieving high pattern recognition rate(82.39%)due to the linear and symmetric synaptic plasticity.This work provides insights into high-sensitivity artificial synapses for future neuromorphic computing.展开更多
Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence.The memristor is an ideal artificial synaptic device with fast operation and g...Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence.The memristor is an ideal artificial synaptic device with fast operation and good tolerance.Here,we have prepared a memristor device with Au/CsPbBr_(3)/ITO structure.The memristor device exhibits resistance switching behavior,the high and low resistance states no obvious decline after 400 switching times.The memristor device is stimulated by voltage pulses to simulate biological synaptic plasticity,such as long-term potentiation,long-term depression,pair-pulse facilitation,short-term depression,and short-term potentiation.The transformation from short-term memory to long-term memory is achieved by changing the stimulation frequency.In addition,a convolutional neural network was constructed to train/recognize MNIST handwritten data sets;a distinguished recognition accuracy of~96.7%on the digital image was obtained in 100 epochs,which is more accurate than other memristor-based neural networks.These results show that the memristor device based on CsPbBr3 has immense potential in the neuromorphic computing system.展开更多
Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it ...Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.展开更多
AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by ...AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.展开更多
In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.I...In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.In particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor line.Chip manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line process.In this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device performance.After a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide platforms.Next,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode operation.Finally,the fabrication of photonic and photonic–electronic neuromorphic computing systems is reviewed.These systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern recognition.With large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.展开更多
Two-dimensional(2D)transition metal chalcogenides(TMC)and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices,particularly futuristic memristive and synapti...Two-dimensional(2D)transition metal chalcogenides(TMC)and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices,particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems.The distinct properties such as high durability,electrical and optical tunability,clean surface,flexibility,and LEGO-staking capability enable simple fabrication with high integration density,energy-efficient operation,and high scalability.This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications,including the promise of 2D TMC materials and heterostructures,as well as the state-of-the-art demonstration of memristive devices.The challenges and future prospects for the development of these emerging materials and devices are also discussed.The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.展开更多
Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture.This computing is realized based on memri...Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture.This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units.Mimicking synaptic functions with these devices is critical in neuromorphic systems.In the last decade,electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions.In this review,these devices are discussed by categorizing them into electrically stimulated,optically stimulated,and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.The working mechanisms of the devices are analyzed in detail.This is followed by a discussion of the progress in mimicking synaptic functions.In addition,existing application scenarios of various synaptic devices are outlined.Furthermore,the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.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
Ion based synaptic devices(ISDs)are one of the excellent candidates for neuromorphic computing.However,most of ISDs utilized additional ion sources to supply ions for adjusting the conductance of the device channel,wh...Ion based synaptic devices(ISDs)are one of the excellent candidates for neuromorphic computing.However,most of ISDs utilized additional ion sources to supply ions for adjusting the conductance of the device channel,which might hinder the large-scale integration for fabricating hierarchical artificial neural network.Here a high-performance monolayer MoS_(2) ISD is demonstrated using Na^(+)ions doped in MoS_(2) lattice as ion sources.Benefited from the Na^(+)ions and S vacancy defects in the MoS_(2) lattice,the device not only exhibits various synaptic plasticity(long-and short-term plasticity)and typical biological features(pain-perceptual nociceptors and associative learning),but also has a low synaptic event response voltage(100 mV)and a low energy consumption(0.92 pJ)for a synaptic event.A dissociation-adsorptionmigration-binding model is proposed to elaborate the resistance switching mechanism,which is corroborated by density functional theory calculations and characterizations.In addition,an artificial neural network(ANN)based on MoS_(2) ISDs is simulated for the recognition of the MNIST handwritten digits.The deviation of the recognition accuracy is less than 8%compared to the ideal floating-point numeric precision.These results provide a new strategy for fabricating high-performance ISDs for neuromorphic computing.展开更多
Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switchi...Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.展开更多
Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing.This study presents ZnO-based artificial synapses with pept...Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing.This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses.We demonstrated the dynamic responses of the device under various environmental conditions.The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices.The transition from short-term memory to longterm memory is achieved via electrochemical doping of ZnO by protons.Furthermore,we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing.The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images.This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems.展开更多
Current-induced multilevel magnetization switching in ferrimagnetic spintronic devices is highly pursued for the application in neuromorphic computing.In this work,we demonstrate the switching plasticity in Co/Gd ferr...Current-induced multilevel magnetization switching in ferrimagnetic spintronic devices is highly pursued for the application in neuromorphic computing.In this work,we demonstrate the switching plasticity in Co/Gd ferrimagnetic multilayers where the binary states magnetization switching induced by spin–orbit toque can be tuned into a multistate one as decreasing the domain nucleation barrier.Therefore,the switching plasticity can be tuned by the perpendicular magnetic anisotropy of the multilayers and the in-plane magnetic field.Moreover,we used the switching plasticity of Co/Gd multilayers for demonstrating spike timing-dependent plasticity and sigmoid-like activation behavior.This work gives useful guidance to design multilevel spintronic devices which could be applied in high-performance neuromorphic computing.展开更多
Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consump...Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consumption greatly.However,one critical challenge is to make the microwave emission frequency of the STO stay constant with a varying input current.In this work,we study the microwave emission characteristics of STOs based on magnetic tunnel junction with MgO cap layer.By applying a small magnetic field,we realize the invariability of the microwave emission frequency of the STO,making it qualified to act as artificial neuron.Furthermore,we have simulated an artificial neural network using STO neuron to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database,and obtained a high accuracy of 92.28%.Our work paves the way for the development of radio-frequency-oriented neuromorphic computing systems.展开更多
基金supported by the Natural Science Foundation of Zhejiang Province(Grant No.LQ24F040007)the National Natural Science Foundation of China(Grant No.U22A2075)the Opening Project of State Key Laboratory of Polymer Materials Engineering(Sichuan University)(Grant No.sklpme2024-1-21).
文摘To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Funded by Basic Research Program of Jiangsu(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘As an emerging memory device,memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption.This review paper focuses on the application of low-power-based memristors in various aspects.The concept and structure of memristor devices are introduced.The selection of functional materials for low-power memristors is discussed,including ion transport materials,phase change materials,magnetoresistive materials,and ferroelectric materials.Two common types of memristor arrays,1T1R and 1S1R crossbar arrays are introduced,and physical diagrams of edge computing memristor chips are discussed in detail.Potential applications of low-power memristors in advanced multi-value storage,digital logic gates,and analogue neuromorphic computing are summarized.Furthermore,the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.
基金supported by the 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financially supported by the Ministry of Education(Singapore)(MOE-T2EP50220-0022)SUTD-MIT International Design Center(Singapore)+3 种基金SUTD-ZJU IDEA Grant Program(SUTD-ZJU(VP)201903)SUTD Kickstarter Initiative(SKI 2021_02_03,SKI 2021_02_17,SKI 2021_01_04)Agency of Science,Technology and Research(Singapore)(A20G9b0135)National Supercomputing Centre(Singapore)(15001618)。
文摘Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)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.
文摘The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.
基金supported by the National Key Research and Development Program of China (2021YFA1202600)the NSFC (92064009, 22175042)+3 种基金the Science and Technology Commission of Shanghai Municipality (22501100900)the China Postdoctoral Science Foundation (2022TQ0068, 2023M740644)the Shanghai Sailing Program (23YF1402200, 23YF1402400)the Qilu Young Scholar Program of Shandong University。
文摘Photoelectric synaptic devices could emulate synaptic behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation and low crosstalk. In this study, we introduced a novel InGaZnO-based photoelectric memristor. Under both electrical and optical stimulation, the device successfully emulated synaptic characteristics including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD). Furthermore, we demonstrated the practical application of our synaptic devices through the recognition of handwritten digits. The devices have successfully shown their ability to modulate synaptic weights effectively through light pulse stimulation, resulting in a recognition accuracy of up to 93.4%. The results illustrated the potential of IGZO-based memristors in neuromorphic computing, particularly their ability to simulate synaptic functionalities and contribute to image recognition tasks.
基金supported by the National Science Fund for Distinguished Young Scholars of China(No.T2125005)the National Key R&D Program of China(Nos.2022YFE0198200,2022YFA1204500,2022YFA1204504)+3 种基金the Shenzhen Science and Technology Project(No.JCYJ20210324121002008)the Natural Science Foundation of Tianjin(Nos.22JCYBJC01290,23JCQNJC01440)the Key Project of Nature Science Foundation of Tianjin(No.22JCZDJC00120)the Fundamental Research Funds for the Central Universities,Nankai University(Nos.BEG124901,BEG124401)。
文摘Artificial synapses are essential building blocks for neuromorphic electronics.Here,solid polymer electrolyte-gated artificial synapses(EGASs)were fabricated using ITO fibers as channels,which possess an ultra-high sensitivity of 5 m V and a long-term memory time exceeding 3 min.Notably,digitally printed ITO-fiber arrays exhibit an ultra-high transmittance of approximately 99.67%.Biological synaptic plasticity,such as excitatory postsynaptic current,paired-pulse facilitation,spike frequency-dependent plasticity,and synaptic potentiation and depression,were successfully mimicked using the EGASs.Based on the synaptic properties of the EGASs,an artificial neural network was constructed to perform supervised learning using the Fashion-MNIST dataset,achieving high pattern recognition rate(82.39%)due to the linear and symmetric synaptic plasticity.This work provides insights into high-sensitivity artificial synapses for future neuromorphic computing.
基金sponsored by the National Natural Science Foundation of China(Grant Nos 11574057,and 12172093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607).
文摘Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence.The memristor is an ideal artificial synaptic device with fast operation and good tolerance.Here,we have prepared a memristor device with Au/CsPbBr_(3)/ITO structure.The memristor device exhibits resistance switching behavior,the high and low resistance states no obvious decline after 400 switching times.The memristor device is stimulated by voltage pulses to simulate biological synaptic plasticity,such as long-term potentiation,long-term depression,pair-pulse facilitation,short-term depression,and short-term potentiation.The transformation from short-term memory to long-term memory is achieved by changing the stimulation frequency.In addition,a convolutional neural network was constructed to train/recognize MNIST handwritten data sets;a distinguished recognition accuracy of~96.7%on the digital image was obtained in 100 epochs,which is more accurate than other memristor-based neural networks.These results show that the memristor device based on CsPbBr3 has immense potential in the neuromorphic computing system.
基金supported by the National Key Research and Development Program of China(No.2023YFB4502200)Natural Science Foundation of China(Nos.92164204 and 62374063)the Science and Technology Major Project of Hubei Province(No.2022AEA001).
文摘Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.
基金Project supported in part by the National Key Research and Development Program of China(Grant No.2021YFA0716400)the National Natural Science Foundation of China(Grant Nos.62225405,62150027,61974080,61991443,61975093,61927811,61875104,62175126,and 62235011)+2 种基金the Ministry of Science and Technology of China(Grant Nos.2021ZD0109900 and 2021ZD0109903)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving ElectronicsTsinghua University Initiative Scientific Research Program.
文摘AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.
基金the support of the National Natural Science Foundation of China(Grant No.62204201)。
文摘In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.In particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor line.Chip manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line process.In this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device performance.After a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide platforms.Next,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode operation.Finally,the fabrication of photonic and photonic–electronic neuromorphic computing systems is reviewed.These systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern recognition.With large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.
基金supported by the Characterization platform for advanced materials funded by the Korea Research Institute of Standards and Science(KRISS-2021-GP2021-0011)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government MSIT(2021M3D1A20396541).
文摘Two-dimensional(2D)transition metal chalcogenides(TMC)and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices,particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems.The distinct properties such as high durability,electrical and optical tunability,clean surface,flexibility,and LEGO-staking capability enable simple fabrication with high integration density,energy-efficient operation,and high scalability.This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications,including the promise of 2D TMC materials and heterostructures,as well as the state-of-the-art demonstration of memristive devices.The challenges and future prospects for the development of these emerging materials and devices are also discussed.The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.
基金This work was supported by the National Natural Science Foundation of China(11804166,U1732126,51872145)the China Postdoctoral Science Foundation(2018M630587)+1 种基金the Natural Science Foundation of Jiangsu Province(BK20200760,BK20191472)the Introduction of Talents Project of Nanjing University of Posts and Telecommunications(NY220097).
文摘Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture.This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units.Mimicking synaptic functions with these devices is critical in neuromorphic systems.In the last decade,electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions.In this review,these devices are discussed by categorizing them into electrically stimulated,optically stimulated,and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.The working mechanisms of the devices are analyzed in detail.This is followed by a discussion of the progress in mimicking synaptic functions.In addition,existing application scenarios of various synaptic devices are outlined.Furthermore,the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
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National Natural Science Foundation of China(NSFC)(No.62274021)thanks to eceshi(www.eceshi.com)for the DFT calculations.
文摘Ion based synaptic devices(ISDs)are one of the excellent candidates for neuromorphic computing.However,most of ISDs utilized additional ion sources to supply ions for adjusting the conductance of the device channel,which might hinder the large-scale integration for fabricating hierarchical artificial neural network.Here a high-performance monolayer MoS_(2) ISD is demonstrated using Na^(+)ions doped in MoS_(2) lattice as ion sources.Benefited from the Na^(+)ions and S vacancy defects in the MoS_(2) lattice,the device not only exhibits various synaptic plasticity(long-and short-term plasticity)and typical biological features(pain-perceptual nociceptors and associative learning),but also has a low synaptic event response voltage(100 mV)and a low energy consumption(0.92 pJ)for a synaptic event.A dissociation-adsorptionmigration-binding model is proposed to elaborate the resistance switching mechanism,which is corroborated by density functional theory calculations and characterizations.In addition,an artificial neural network(ANN)based on MoS_(2) ISDs is simulated for the recognition of the MNIST handwritten digits.The deviation of the recognition accuracy is less than 8%compared to the ideal floating-point numeric precision.These results provide a new strategy for fabricating high-performance ISDs for neuromorphic computing.
基金supported in part by China Key Research and Development Program(2016YFA0201800)the National Natural Science Foundation of China(91964104,61974081)。
文摘Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A2C2004864)。
文摘Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing.This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses.We demonstrated the dynamic responses of the device under various environmental conditions.The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices.The transition from short-term memory to longterm memory is achieved via electrochemical doping of ZnO by protons.Furthermore,we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing.The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images.This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems.
基金supported by Beijing Natural Science Foundation Key Program(Grant No.Z190007)Beijing Natural Science Foundation(Grant No.2212048)+1 种基金the National Natural Science Foundation of China(Grant Nos.11474272,61774144,and 12004212)the Chinese Academy of Sciences(Grant Nos.QYZDY-SSW-JSC020,XDB28000000,and XDB44000000)。
文摘Current-induced multilevel magnetization switching in ferrimagnetic spintronic devices is highly pursued for the application in neuromorphic computing.In this work,we demonstrate the switching plasticity in Co/Gd ferrimagnetic multilayers where the binary states magnetization switching induced by spin–orbit toque can be tuned into a multistate one as decreasing the domain nucleation barrier.Therefore,the switching plasticity can be tuned by the perpendicular magnetic anisotropy of the multilayers and the in-plane magnetic field.Moreover,we used the switching plasticity of Co/Gd multilayers for demonstrating spike timing-dependent plasticity and sigmoid-like activation behavior.This work gives useful guidance to design multilevel spintronic devices which could be applied in high-performance neuromorphic computing.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974379 and 12204357)K.C.Wong Education Foundation(Grant No.GJTD2019-14)+2 种基金Jiangxi Province“Double Thousand Plan”(Grant No.S2019CQKJ2638)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.22KB140017)Wuxi University Research Start-up Fund for Introduced Talents(Grant No.2022r006)。
文摘Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consumption greatly.However,one critical challenge is to make the microwave emission frequency of the STO stay constant with a varying input current.In this work,we study the microwave emission characteristics of STOs based on magnetic tunnel junction with MgO cap layer.By applying a small magnetic field,we realize the invariability of the microwave emission frequency of the STO,making it qualified to act as artificial neuron.Furthermore,we have simulated an artificial neural network using STO neuron to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database,and obtained a high accuracy of 92.28%.Our work paves the way for the development of radio-frequency-oriented neuromorphic computing systems.
