Application of artificial synapse based on all-inorganic perovskite memristor in neuromorphic computing

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.

Complementary memtransistors for neuromorphic computing: How, what and why

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.

Advances in neuromorphic computing:Expanding horizons for AI development through novel artificial neurons and in-sensor computing

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.

InGaZnO-based photoelectric synaptic devices for neuromorphic computing

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.

Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing

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.

Spin torque oscillator based on magnetic tunnel junction with MgO cap layer for radio-frequency-oriented neuromorphic computing

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.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

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.

Memristive Artificial Synapses for Neuromorphic Computing

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.

Towards engineering in memristors for emerging memory and neuromorphic computing: A review

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.

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

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.

Switching plasticity in compensated ferrimagnetic multilayers for neuromorphic computing

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.

Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

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.

Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing

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.

SemiSynBio:A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing has seen ongoing development with the rise of synthetic biology,becoming the driving force for new generation semiconductor synthetic biology(SemiSynBio)technologies.DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks(ANNs),providing the possibility of executing neuromorphic computing at the molecular level.Herein,we briefly outline the principles of neuromorphic computing,describe the advances in DNA computing with a focus on synthetic neuromorphic computing,and summarize the major challenges and prospects for synthetic neuromorphic computing.We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing,which would be of widespread use in biocomputing,DNA storage,information security,and national defense.

CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review

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.

Ultrasensitive solar-blind ultraviolet detection and optoelectronic neuromorphic computing using α-In_(2)Se_(3)phototransistors

Detection of solar-blind ultraviolet(SB-UV)light is important in applications like confidential communication,flame detection,and missile warning system.However,the existing SB-UV photodetectors still show low sensitivities.In this work,we demonstrate the extraordinary SB-UV detection performance of α-In_(2)Se_(3 )phototransistors.Benefiting from the coupled semiconductor and ferroelectricity property,the phototransistor has an ultraweak detectable power of 17.85 fW,an ultrahigh gain of 1.2×10^(6),a responsivity of 2.6×10^(5) A/W,a detectivity of 1.3×10^(16) Jones and an ultralow noise-equivalent-power of 4.2×10^(–20 )W/Hz1/2 for 275 nm light.Its performance exceeds most other UV detectors,even including commercial photomultiplier tubes and avalanche photodiodes.It can be also implemented as an optoelectronic synapse for neuromorphic computing.A 784×300×10 artificial neural network(ANN)based on this optoelectronic synapse is constructed and demonstrated with a high recognition accuracy and good noise-tolerance for the Fashion-MNIST dataset.These extraordinary features endow this phototransistor with the potential for constructing advanced SB-UV detectors and intelligent hardware.

Reconfigurable memristor based on SrTiO_(3) thin-film for neuromorphic computing

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain.However,the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail.In this work,Ag/SrTiO_(3)(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip.By modulating the compliance current,two types of resistance switching,volatile and nonvolatile,can be obtained in amorphous STO thin film.This is attributed to the manipulation of the Ag conductive filament.Furthermore,through regulating electrical pulses and designing bionic circuits,the neuronal functions of leaky integrate and fire,as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation,are successfully realized.This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

Optically modulated dual-mode memristor arrays based on core-shell CsPbBr_(3)@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware

Artificial synapses and neurons are crucial milestones for neuromorphic computing hardware,and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the construction of hardware neural networks.However,most of the memristors can only operate in one mode,that is,resistive switching or threshold switching,and distinct memristors are required to construct fully memristive neuromorphic computing hardware,making it more complex for the fabrication and integration of the hardware.Herein,we propose a flexible dual-mode memristor array based on core–shell CsPbBr3@graphdiyne nanocrystals,which features a 100%transition yield,small cycle-to-cycle and device-to-device variability,excellent flexibility,and environmental stability.Based on this dual-mode memristor,homo-material-based fully memristive neuromorphic computing hardware—a power-free artificial nociceptive signal processing system and a spiking neural network—are constructed for the first time.Our dual-mode memristors greatly simplify the fabrication and integration of fully memristive neuromorphic systems.

Van der Waals materials-based floating gate memory for neuromorphic computing

With the advent of the“Big Data Era”,improving data storage density and computation speed has become more and more urgent due to the rapid growth in different types of data.Flash memory with a floating gate(FG)structure is attracting great attention owing to its advantages of miniaturization,low power consumption and reli-able data storage,which is very effective in solving the problems of large data capacity and high integration density.Meanwhile,the FG memory with charge storage principle can simulate synaptic plasticity perfectly,breaking the traditional von Neumann computing ar-chitecture and can be used as an artificial synapse for neuromorphic computations inspired by the human brain.Among many candidate materials for manufacturing devices,van der Waals(vdW)materials have attracted widespread attention due to their atomic thickness,high mobility,and sustainable miniaturization properties.Owing to the arbitrary stacking ability,vdW heterostructure combines rich physics and potential 3D integration,opening up various possibilities for new functional integrated devices with low power consumption and flexible applications.This paper provides a comprehensive review of memory devices based on vdW materials with FG structure,including the working principles and typical structures of FG structure devices,with a focus on the introduction of various highperformance FG memories and their versatile applications in neuro-morphic computing.Finally,the challenges of neuromorphic devices based on FG structures are also discussed.This review will shed light on the design and fabrication of vdW material-based memory devices with FG engineering,helping to promote the development of practical and promising neuromorphic computing.

Monolayer MoS_(2)synaptic devices synergistically modulated by Na^(+)ions and sulfur vacancies for neuromorphic computing and pain perception stimulation

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.