Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Hongyu Bian; Yi Yiing Goh; Yuxia Liu; Haifeng Ling; Linghai Xie; Xiaogang Liu

doi:10.1002/adma.202006469

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Hongyu Bian, Yi Yiing Goh, Yuxia Liu, Haifeng Ling, Linghai Xie, Xiaogang Liu

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	2006469
Journal	Advanced Materials
DOIs	https://doi.org/10.1002/adma.202006469
State	Published - Apr 9 2021
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-06-23
Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736
Acknowledgements: The Ministry of Education, Singapore (MOE2017-T2-2-110), Agency for Science, Technology and Research (A*STAR) under its AME program (Grant NO. A1883c0011 and A1983c0038), National Research Foundation, the Prime Minister's Office of Singapore under its NRF Investigatorship Programme (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the National Natural Science Foundation of China (21771135, 21871071, 21774061, 61905121), and the Natural Science Foundation of Jiangsu Province, China (No. BK20190734)) are acknowledged
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Mechanics of Materials
General Materials Science
Mechanical Engineering

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10.1002/adma.202006469

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title = "Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing",

abstract = "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.",

author = "Hongyu Bian and Goh, {Yi Yiing} and Yuxia Liu and Haifeng Ling and Linghai Xie and Xiaogang Liu",

note = "KAUST Repository Item: Exported on 2021-06-23 Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736 Acknowledgements: The Ministry of Education, Singapore (MOE2017-T2-2-110), Agency for Science, Technology and Research (A*STAR) under its AME program (Grant NO. A1883c0011 and A1983c0038), National Research Foundation, the Prime Minister's Office of Singapore under its NRF Investigatorship Programme (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the National Natural Science Foundation of China (21771135, 21871071, 21774061, 61905121), and the Natural Science Foundation of Jiangsu Province, China (No. BK20190734)) are acknowledged This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2021",

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doi = "10.1002/adma.202006469",

language = "English (US)",

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journal = "Advanced Materials",

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AU - Bian, Hongyu

AU - Goh, Yi Yiing

AU - Liu, Yuxia

AU - Ling, Haifeng

AU - Xie, Linghai

AU - Liu, Xiaogang

N1 - KAUST Repository Item: Exported on 2021-06-23 Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736 Acknowledgements: The Ministry of Education, Singapore (MOE2017-T2-2-110), Agency for Science, Technology and Research (A*STAR) under its AME program (Grant NO. A1883c0011 and A1983c0038), National Research Foundation, the Prime Minister's Office of Singapore under its NRF Investigatorship Programme (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the National Natural Science Foundation of China (21771135, 21871071, 21774061, 61905121), and the Natural Science Foundation of Jiangsu Province, China (No. BK20190734)) are acknowledged This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2021/4/9

Y1 - 2021/4/9

N2 - 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.

AB - 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.

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Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Abstract

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