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
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
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
Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient,low-power,and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.A...Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient,low-power,and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses,enabling the hardware implementation of artificial neural networks.Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching devices and electric-double-layer transistors.These devices have demonstrated a range of neuromorphic functions such as multistate storage,spike-timing-dependent plasticity,dynamic filtering,etc.To achieve high performance neuromorphic computing systems,it is essential to fabricate neuromorphic devices compatible with the complementary metal oxide semiconductor(CMOS)manufacturing process.This improves the device’s reliability and stability and is favorable for achieving neuromorphic chips with higher integration density and low power consumption.This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing.We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.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
The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and adv...The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced robotics.Leveraging 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption.Here,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia detection.With experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,respectively.The optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios.Moreover,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision application.To display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory functions.The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.展开更多
Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex asso...Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.展开更多
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana...Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.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
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
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
The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerg...The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.展开更多
To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which...To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which can be reconfigured as synapses or LIF neurons.The ionic dynamic doping contributed to the resistance changes of VO_(2),which enables the reversible modulation of device states.The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration.Based on the reconfigurable EC-VO_(2),the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons,whose final accuracy reached 91.92%.展开更多
Traditional computing structures are blocked by the von Neumann bottleneck,and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention.H...Traditional computing structures are blocked by the von Neumann bottleneck,and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention.Here,a flexible organic device with 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene(C8-BTBT)and 2,9-didecyldinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(C10-DNTT)heterostructural channel having excellent synaptic behaviors was fabricated on muscovite(MICA)substrate,which has a memory window greater than 20 V.This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel.Furthermore,the device simulates organism synaptic behaviors successfully,such as paired-pulse facilitation(PPF),long-term potentiation/depression(LTP/LTD)process,and transition from short-term memory(STM)to long-term memory(LTM)by optical and electrical modulations.Importantly,the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology(MNIST)pattern recognition,with a recognition rate nearly 100%without noise.This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.展开更多
The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive ...The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.展开更多
Memristive devices have attracted intensive attention in developing hardware neuromorphic computing systems with high energy efficiency due to their simple structure,low power consumption,and rich switching dynamics r...Memristive devices have attracted intensive attention in developing hardware neuromorphic computing systems with high energy efficiency due to their simple structure,low power consumption,and rich switching dynamics resembling biological synapses and neurons in the last decades.Fruitful demonstrations have been achieved in memristive synapses neurons and neural networks in the last few years.Versatile dynamics are involved in the data processing and storage in biological neurons and synapses,which ask for carefully tuning the switching dynamics of the memristive emulators.Note that switching dynamics of the memristive devices are closely related to switching mechanisms.Herein,from the perspective of switching dynamics modulations,the mainstream switching mechanisms including redox reaction with ion migration and electronic effect have been systemically reviewed.The approaches to tune the switching dynamics in the devices with different mechanisms have been described.Finally,some other mechanisms involved in neuromorphic computing are briefly introduced.展开更多
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 Natural Science Foundation of China(Grant Nos.62074075,62174082,and 61834001).
文摘Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient,low-power,and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses,enabling the hardware implementation of artificial neural networks.Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching devices and electric-double-layer transistors.These devices have demonstrated a range of neuromorphic functions such as multistate storage,spike-timing-dependent plasticity,dynamic filtering,etc.To achieve high performance neuromorphic computing systems,it is essential to fabricate neuromorphic devices compatible with the complementary metal oxide semiconductor(CMOS)manufacturing process.This improves the device’s reliability and stability and is favorable for achieving neuromorphic chips with higher integration density and low power consumption.This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing.We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.
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.
基金financial support by the Semiconductor Initiative at the King Abdullah University of Science and Technologysupported by King Abdullah University of Science and Technology(KAUST)Research Funding(KRF)under Award No.ORA-2022-5314.
文摘The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced robotics.Leveraging 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption.Here,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia detection.With experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,respectively.The optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios.Moreover,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision application.To display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory functions.The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.
基金This work was supported by the Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017)National Research Foundation of Korea(NRF)grant funded by the Korea government(MIST)(RS-2023-00302751)+1 种基金by the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grants 2018R1A6A1A03025242 and 2018R1D1A1A09083353by Qilu Young Scholar Program of Shandong University.
文摘Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227,62076208, and 62076207)Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257)+3 种基金National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309)the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020)College of Artificial Intelligence, Southwest UniversityState Key Laboratory of Intelligent Vehicle Safety Technology
文摘Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.
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 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This work was supported in part by the Ministry of Science and Technology of China under Grant No.2017YFA0206102in part by the National Natural Science Foundation of China under Grant No.61922083+2 种基金by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDB44000000by the European Union’s Horizon 2020 Research and Innovation Program with Grant Agreement No.824164by the German Research Foundation Projects MemCrypto under Grant No.GZ:DU 1896/2-1 and MemDPU under Grant No.GZ:DU 1896/3-1.
文摘The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61925401,92064004,61927901,and 92164302)the 111 Project (Grant No.B18001)+1 种基金support from the Fok Ying-Tong Education Foundationthe Tencent Foundation through the XPLORER PRIZE。
文摘To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which can be reconfigured as synapses or LIF neurons.The ionic dynamic doping contributed to the resistance changes of VO_(2),which enables the reversible modulation of device states.The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration.Based on the reconfigurable EC-VO_(2),the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons,whose final accuracy reached 91.92%.
基金the National Key Research and Development Program of China(No.2021YFA1202600)the National Natural Science Foundation of China(Nos.92064009 and 22175042)+3 种基金the Science and Technology Commission of Shanghai Municipality(No.22501100900)the China Postdoctoral Science Foundation(Nos.2022TQ0068 and 2023M740644)the Shanghai Sailing Program(Nos.23YF1402200 and 23YF1402400)Jiashan Fudan Institute.
文摘Traditional computing structures are blocked by the von Neumann bottleneck,and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention.Here,a flexible organic device with 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene(C8-BTBT)and 2,9-didecyldinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(C10-DNTT)heterostructural channel having excellent synaptic behaviors was fabricated on muscovite(MICA)substrate,which has a memory window greater than 20 V.This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel.Furthermore,the device simulates organism synaptic behaviors successfully,such as paired-pulse facilitation(PPF),long-term potentiation/depression(LTP/LTD)process,and transition from short-term memory(STM)to long-term memory(LTM)by optical and electrical modulations.Importantly,the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology(MNIST)pattern recognition,with a recognition rate nearly 100%without noise.This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.
基金supported by the National Natural Science Foundation of China(61927802,61722209,and 61805145)the Beijing Municipal Science and Technology Commission(Z181100003118014)+3 种基金the National Key Research and Development Program of China(2020AAA0130000)the support from the National Postdoctoral Program for Innovative TalentShuimu Tsinghua Scholar Programthe support from the Hong Kong Research Grants Council(16306220)。
文摘The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1832116 and 51772112)Fundamental Research Funds for the Central Universities,China(Grant No.HUST:2016YXZD058).
文摘Memristive devices have attracted intensive attention in developing hardware neuromorphic computing systems with high energy efficiency due to their simple structure,low power consumption,and rich switching dynamics resembling biological synapses and neurons in the last decades.Fruitful demonstrations have been achieved in memristive synapses neurons and neural networks in the last few years.Versatile dynamics are involved in the data processing and storage in biological neurons and synapses,which ask for carefully tuning the switching dynamics of the memristive emulators.Note that switching dynamics of the memristive devices are closely related to switching mechanisms.Herein,from the perspective of switching dynamics modulations,the mainstream switching mechanisms including redox reaction with ion migration and electronic effect have been systemically reviewed.The approaches to tune the switching dynamics in the devices with different mechanisms have been described.Finally,some other mechanisms involved in neuromorphic computing are briefly introduced.