Emerging Materials for Neuromorphic Devices and Systems

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Abstract

Neuromorphic devices and systems have attracted attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide-semiconductor-based hardware. However, these approaches have drawbacks in power consumption and learning speed. An energy-efficient neuromorphic computing system requires hardware that can mimic the functions of a brain. Therefore, various materials have been introduced for the development of neuromorphic devices. Here, recent advances in neuromorphic devices are reviewed. First, the functions of biological synapses and neurons are discussed. Also, deep neural networks and spiking neural networks are described. Then, the operation mechanism and the neuromorphic functions of emerging devices are reviewed. Finally, the challenges and prospects for developing neuromorphic devices that use emerging materials are discussed.

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Devices; Electronic Materials; Materials Design; Memory Structure

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Most cited references 140

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Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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Short-term synaptic plasticity.

Robert S. Zucker, Wade Regehr (2002)

Synaptic transmission is a dynamic process. Postsynaptic responses wax and wane as presynaptic activity evolves. This prominent characteristic of chemical synaptic transmission is a crucial determinant of the response properties of synapses and, in turn, of the stimulus properties selected by neural networks and of the patterns of activity generated by those networks. This review focuses on synaptic changes that result from prior activity in the synapse under study, and is restricted to short-term effects that last for at most a few minutes. Forms of synaptic enhancement, such as facilitation, augmentation, and post-tetanic potentiation, are usually attributed to effects of a residual elevation in presynaptic [Ca(2+)]i, acting on one or more molecular targets that appear to be distinct from the secretory trigger responsible for fast exocytosis and phasic release of transmitter to single action potentials. We discuss the evidence for this hypothesis, and the origins of the different kinetic phases of synaptic enhancement, as well as the interpretation of statistical changes in transmitter release and roles played by other factors such as alterations in presynaptic Ca(2+) influx or postsynaptic levels of [Ca(2+)]i. Synaptic depression dominates enhancement at many synapses. Depression is usually attributed to depletion of some pool of readily releasable vesicles, and various forms of the depletion model are discussed. Depression can also arise from feedback activation of presynaptic receptors and from postsynaptic processes such as receptor desensitization. In addition, glial-neuronal interactions can contribute to short-term synaptic plasticity. Finally, we summarize the recent literature on putative molecular players in synaptic plasticity and the effects of genetic manipulations and other modulatory influences.

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Nanoscale memristor device as synapse in neuromorphic systems.

Sung Hyun Jo, Ting Hao Chang, Idongesit Ebong … (2010)

A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.

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Author and article information

Contributors

Jang-Sik Lee

Journal

Journal ID (nlm-ta): iScience

Journal ID (iso-abbrev): iScience

Title: iScience

Publisher: Elsevier

ISSN (Electronic): 2589-0042

Publication date PMC-release: 24 November 2020

Publication date Collection: 18 December 2020

Publication date (Electronic): 24 November 2020

Volume: 23

Issue: 12

Electronic Location Identifier: 101846

Affiliations

[1 ]Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea

Author notes

[∗ ]Corresponding author jangsik@ 123456postech.ac.kr

[2]

These authors contributed equally

Article

Publisher Item ID: S2589-0042(20)31043-9 Publisher ID: 101846

DOI: 10.1016/j.isci.2020.101846

PMC ID: 7725950

PubMed ID: 33319174

SO-VID: cdce504c-c296-49f5-a7c4-7a74bd9669c4

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Emerging Materials for Neuromorphic Devices and Systems

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Most cited references 140

Deep learning.

Short-term synaptic plasticity.

Nanoscale memristor device as synapse in neuromorphic systems.

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