Neuromorphic Computing System Research Articles

Orientation-Selective Memory Switching in Quasi-1D NbSe3 Neuromorphic Device for Omnibearing Motion Detection.

Intelligent neuromorphic hardware holds considerable promise in addressing the growing demand for massive real-time data processing in edge computing. Resistive switching materials with intrinsic anisotropy and a compact design of non-volatile memory devices with the capability of handling spatiotemporally reconstructed data is crucial to perform sophisticated tasks in complex application scenarios. In this study, an anisotropic resistive switching cell with a planar configuration based on lithiated NbSe3 nanosheets is demonstrated. Benefitting from the highly aligned diffusive channel associated with a quasi-1D van der Waals structure, the memristor patterned along NbSe3 atomic chains presents robust memory switching behavior with superior stability, particularly the low set/reset voltages (0.4V/-0.36V) and extremely small standard deviation (0.041V/0.051V), among the best compared to state-of-the-art devices. More importantly, unlike traditional resistive switching materials, anisotropic ion migration in NbSe3 crystals leads to a high orientation selectivity in the conductance update. Custom-designed neuromorphic hardware contributes to the implementation of omnibearing motion recognition for automatic pilot applications, yielding a high accuracy of 95.9% considering variations. This article presents a new strategy based on NbSe3 crystals to develop a neuromorphic computing system with intelligent application scenarios.

Neuromorphic Computing for Smart Agriculture

Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future.

Synaptic Properties of an Interfacial Memristor Based on a Ga2O3/Nb:SrTiO3 Heterojunction.

Memristors have been extensively studied for tremendous potential for future neuromorphic computing hardware applications because of their ability to imitate biological synaptic processes. Herein, we report an interfacial memristor based on a Ga2O3/Nb:SrTiO3 heterojunction that shows stable bipolar resistive switching behavior, long retention time, and high switching ratio. The conductance of the Au/Ga2O3/Nb:SrTiO3/In memristor can be gradually modulated under the voltage sweep mode as well as positive and negative pulse voltage stimulations, respectively, thus realizing the long-term potentiation/depression characteristics of the simulated biological synapse. A neural network based on the prepared memristor was built to recognize the handwritten picture data set with a recognition accuracy of 92.78% by using the NeuroSimV3.0 platform. Our work indicates that the Ga2O3/Nb:SrTiO3 heterojunction memristor has significant potential in a neuromorphic computing system.

Simulation-based effective comparative analysis of neuron circuits for neuromorphic computation systems

The spiking neural networks (SNN) that are inspired by the human brain offers wider scope for application in the growth of neuromorphic computing systems due to their brain level computational capabilities, reduced power consumption, and minimal data movement cost, among other advantages. Spike-based neurons and synapses are the essential building blocks of SNN, and their efficient implementation is vital to their performance enhancement. In this regard, the design and implementation of spiking neurons have been the major focus among the researchers. In this paper, functioning of different leaky integrate fire (LIF)-based spiking neuron circuits like frequency adaptable CMOS-based LIF, resistor-capacitor-based (RC) LIF, and volatile memristor-based LIF are subjected to comparison. The work mainly focuses on revealing analysis of spike duration and amplitude, number of spikes produced during excitation period, threshold operation, field of application, and various other significant parameters of aforementioned neuron circuits. Extensive simulations of these circuits are carried out utilizing the Cadence Virtuoso simulation environment in order to validate their behavior. Further, a brief comparative analysis is executed considering into account the attributes like circuit complexity, supply voltage, firing rate, membrane capacitance, nature of input/output, refractory mechanism, and energy consumption per spike. This work seeks to assist researchers in selecting an appropriate LIF model to efficiently construct memristors and/or non-memristors based SNN for certain application.

Organic Ferroelectrics for Regulation of Electronic and Ionic Transport Toward Neuromorphic Applications

AbstractOrganic ferroelectrics (OFEs) have been of significant research interest not only for nonvolatile memory applications but also for their unique material characteristics such as mechanical softness, biocompatibility, facile processibility, and chemically tailorable functionalities that inorganic counterparts are hard to achieve. Despite these promising merits, the utilization of OFEs has mainly focused on simply demonstrating flexible nonvolatile memories wherein modulation of electronic conductance is of interest. Recent studies indicate that the applicability of OFEs can be further extensive, particularly when combined with electronic, ionic, and mixed electronic‐ionic conducting media. Herein, we discuss that OFEs can be employed for the regulation of electronic as well as ionic charges, and lead to unique device behaviors. First, we comprehensively introduce organic ferroelectric materials classified with their structures and compositions. Next, we discuss recent studies where organic ferroelectricity has been incorporated with electronic, ionic, or mixed transport system to resolve issues in devices and endow multifunctionality, which are promising for neuromorphic computing and sensory memory systems. Finally, insight into the research direction of OFEs is provided, and what hurdles shall be overcome for real‐world applications.

Optically and electrically modulated artificial synapses based on MoS2/PZT ferroelectric field-effect transistor for neuromorphic computing system

To present an advanced device scheme of high-performance optoelectronic synapses, herein, we demonstrated the electrically- and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-based ferroelectric field-effect transistor (FeFET) architecture. The device was fabricated in the form of the MoS2/PZT FeFET, and its synaptic weights were effectively controlled by dual stimuli (i.e., both electrical and optical pulses simultaneously) as well as single stimuli (i.e., either electrical or optical pulses alone). This could be attributed to the electrical pulse-tunable strong ferroelectric polarization in PbZrxTi1−xO3 (PZT) as well as the polarization field-enhanced persistent photoconductivity effect in MoS2. Additionally, it was confirmed that the proposed device possesses substantial activity, achieving approximately 95 % pattern recognition accuracy. The results substantiate the great potential of the 2D semiconductor channel-based FeFET device as a high-performance optoelectronic synaptic platform, marking a pivotal stride towards the realization of advanced neuromorphic computing systems.

Exploring The Horizon of Neuromorphic Computing: Research Progress of Synaptic Transistor Materials

In recent years, with the rapid development of integrated circuits, Moore's law has become more and more difficult to maintain. The brain-like neuromorphic computing system based on the new brain-like device synaptic transistor provides an alternative way for future computers. At present, a large number of studies have tried to fabricate synaptic transistors with different materials, and simulated synaptic behavior successfully. In this review, the basic structure and function of synaptic transistors are described, and three kinds of materials for fabricating synaptic transistors are classified: organic and non-solid electrolyte materials, inorganic solid materials and organic flexible materials, and their current research is reviewed. The purpose of this review is to provide more research directions for the research in the field by summarizing the current research on the fabrication materials of synaptic transistors. At present, there are still many new materials that have not been tried in the research field of synaptic transistors. Trying to use composite materials and two-dimensional materials is a noteworthy research direction, which is expected to promote the development of a new generation of memory technology and the application in the field of artificial intelligence.

Multimodal In‐Sensor Computing Implemented by Easily‐Fabricated Oxide‐Heterojunction Optoelectronic Synapses

AbstractThe advent of big data and the Internet of Things has created urgent demands for in‐sensor computing hardware with multimodal perception that can effectively resolve the inefficiency, high latency, and excessive energy consumption challenges faced by conventional sensory systems. Here, a simple‐structured optoelectronic synaptic device of In2O3·SnO2/Nb:SrTiO3 (ITO/NSTO) heterostructure is proposed, which vividly demonstrates in‐sensor computing and multimodal perception capabilities. First, with the ingenious synaptic responses of the device under both optical and electrical stimuli, a multimodal in‐sensor neuromorphic computing system capable of concurrently perceiving and processing visual and auditory information is constructed. Using this multimodal system to perform a human emotion recognition task, the misjudgment arising from single‐modal cognition can thereby be effectively avoided. Second, utilizing the integrated sensing and processing functions, along with the dynamic memories of the device array, a neuromorphic vision system is implemented for real‐time monitoring of moving vehicles, which displays high recognition efficiency and accuracy. This research not only provides an optoelectronic synaptic device that is low‐cost and easy to mass‐produce, but also paves the way for next‐generation in‐sensor computing that can efficiently process multimodal signals.

MLG-NCS: Multimodal Local–Global Neuromorphic Computing System for Affective Video Content Analysis

MLG-NCS: Multimodal Local–Global Neuromorphic Computing System for Affective Video Content Analysis

Linear Synaptic Weight Update in Selector-Less HfO₂ RRAM Using Al₂O₃ Built-In Resistor for Neuromorphic Computing Systems

Linear Synaptic Weight Update in Selector-Less HfO₂ RRAM Using Al₂O₃ Built-In Resistor for Neuromorphic Computing Systems

Topological hydrogen-bonded organic frameworks (HOFs) and their electronic applications in sensor, memristor, and neuromorphic computing

Recently, an emerging class of hydrogen-bonded organic frameworks (HOFs) has become an appealing member of organic material family, attributed to their layered self-assembly structures, high-crystalline, and environmentally friendly characteristics, which have rapidly propelled their development in the field of electronic devices. In this context, we focus on the latest category of topological HOFs, with particular attention given to cutting-edge experimental demonstrations, exceptional electrical performances, and promising applications. First, a concise concept and fundamental mechanism of HOFs are provided, elucidating the potential correlation between structural designs and material properties. Subsequently, a comprehensive summary is presented on the preparation and synthesis methods, such as hydrothermal techniques, epitaxial growth, electro-deposition, among others. Notably, the latest advancements in HOFs-based electronics are thoroughly introduced and discussed, along with their applications in sensors, memristors, artificial synapses, neuromorphic computing, and human perception systems. Finally, the future challenges and prospects of topological HOFs are elaborated upon with the aim of providing valuable guidance for high-performance HOF-based electronics.

A CuOx/p+-Si memristor with short- and long-term plasticity for homogeneous reservoir computing system

Reservoir computing (RC) system is a simple and cost-efficient neuromorphic computing system mainly consisting of a reservoir layer and a readout layer. The reservoir layer comprises nonlinear nodes with short-term plasticity (STP), while the readout layer comprises linear nodes with long-term plasticity (LTP). Here, we propose a self-rectifying (>102) memristor based on CuOx/p+-Si heterojunction that exhibits both nonlinear STP and linear LTP characteristics with high uniformity (σ/μ = 2.8 %). The resistive switching of the device is mainly based on electron trapping/detrapping in CuOx film. The trapping before relaxation is very unstable and less capable of long-term trapping, which leads to STP. The trapping after relaxation is relatively stable for the lowered trap barrier, which induces LTP. Utilizing its dynamic STP characteristics for the reservoir layer and LTP properties for the readout layer, a CuOx/p+-Si memristor-based homogeneous dynamic RC system was constructed for a spoken-digital recognition task, yielding an accuracy rate of 95.33 %. These results affirm the tremendous potential of our device in establishing a highly compact and full-memristor-based homogeneous RC system.

Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing

The development of the Internet of Things (IoT) not only facilitates our lives but also dramatically grows data. Artificial synaptic devices appear to represent an emerging physical solution compared to the existing computational architectures. Memristive devices are of great interest with their high integration and low power consumption in the field of synaptic devices. In this work, we demonstrate short-term plasticity (STP) and long-term plasticity (LTP) by regulating synaptic weights with different stimulus pulses. Moreover, the application in neuromorphic computing is further exhibited by image recognition with an accuracy of 95.2 % under the modified National Institute of Standards and Technology database. Therefore, Nd-doped Bi4Ti3O12 memristors, as emerging artificial synaptic devices, are expected to achieve breakthroughs in artificial intelligence and promote the development of neuromorphic computing and intelligent systems.

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems.

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

Temperature effect on a weighted vortex spin-torque nano-oscillator for neuromorphic computing

In this work, we present fabricated magnetic tunnel junctions (MTJs) that can serve as magnetic memories (MMs) or vortex spin-torque nano-oscillators (STNOs) depending on the device geometry. We explore the heating effect on the devices to study how the performance of a neuromorphic computing system (NCS) consisting of MMs and STNOs can be enhanced by temperature. We further applied a neural network for waveform classification applications. The resistance of MMs represents the synaptic weights of the NCS, while temperature acts as an extra degree of freedom in changing the weights and TMR, as their anti-parallel resistance is temperature sensitive, and parallel resistance is temperature independent. Given the advantage of using heat for such a network, we envision using a vertical-cavity surface-emitting laser (VCSEL) to selectively heat MMs and/or STNO when needed. We found that when heating MMs only, STNO only, or both MMs and STNO, from 25 to 75 °C, the output power of the STNO increases by 24.7%, 72%, and 92.3%, respectively. Our study shows that temperature can be used to improve the output power of neural networks, and we intend to pave the way for future implementation of a low-area and high-speed VCSEL-assisted spintronic NCS.

Weight‐Reconfigurable Neuromorphic Computing Systems for Analog Signal Integration

AbstractOwing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi‐input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi‐input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost‐effective and high‐throughput manufacturing. Additionally, the real‐time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing.

Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System.

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hbox {HfO}}_{2}$$\\end{document} based memristive device. 1st\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ extrm{st}$$\\end{document} and 2nd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\ extrm{nd}$$\\end{document} stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 4.3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature.

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200°C. Based on the advanced functions under light stimulus, the constructed 5×3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200°C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

A nanosecond-scale CuI synaptic memristor prepared by a solution-based process

Owing to the synaptic behaviors and functions, memristors are intensively studied as a critical component for the neuromorphic computing system which is considered as an effective scenario to tackle the performance bottleneck existing in modern computers based on the von Neumann architecture. A novel synaptic device base on the CuI memristor prepared with a solution-based process is proposed in this work, and a set of synaptic features are emulated. The electrochemical metallization contributes to the resistive switching behavior and small operating voltages (Vset = 0.67 V and Vreset = −0.33 V). Moreover, the CuI memristor exhibits the small switching energy (23 pJ) and ultraquick switching speed (100 ns). A large CuI memristor array is realized experimentally, by which the hardware-based multiply-and-accumulate (MAC) operation is implemented. Furthermore, the hardware-based convolution computation in image processing is realized based on the MAC operation, demonstrating the potential applications in image processing requiring the high degree of parallelism.