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Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
Keywords: Memristive devices; artificial neurons; neuromorphic computing; neuromorphic sensing; spiking dynamics.
© 2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the âvon Neumann bottleneckâ between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy-and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.
Keywords: artificial synapses; memristive materials; neurons; synaptic plasticity.
© 2021 Wiley-VCH GmbH.
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
Keywords: artificial neural networks; artificial neurons; artificial synapses; memristive electronic devices; memristors; neuromorphic electronics.
© 2020 Wiley-VCH GmbH.
The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 Ă 100 EGTs into a 2.5 Ă 2.5 cm2 glass, with a unit density of up to 1,600 devices cm-2. Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 Ă 10-3 S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.
Keywords: Photo-Lithography; artificial synapse array; electrolyte-gated transistors; lateral-gate; metal-organic framework.
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
