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Nov, 2021;33 (46): e2006469.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing.

Hongyu Bian, Yi Yiing Goh, Yuxia Liu, Haifeng Ling, Linghai Xie, Xiaogang Liu
Author Information
  1. Hongyu Bian: Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore.
  2. Yi Yiing Goh: Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore.
  3. Yuxia Liu: Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore.
  4. Haifeng Ling: Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
  5. Linghai Xie: Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
  6. Xiaogang Liu: Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore. ORCID
PMID: 33837601 DOI: 10.1002/adma.202006469

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.

Keywords

artificial synapses memristive materials neurons synaptic plasticity

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Grants

  1. MOE2017-T2-2-110/Ministry of Education, Singapore
  2. /Agency for Science, Technology and Research
  3. A1883c0011/A*STAR
  4. A1983c0038/A*STAR
  5. NRF-NRFI05-2019-0003/Prime Minister's Office of Singapore
  6. /King Abdullah University of Science and Technology
  7. 2018-CRG7-3736/Office of Sponsored Research
  8. 21771135/National Natural Science Foundation of China
  9. 21871071/National Natural Science Foundation of China
  10. 21774061/National Natural Science Foundation of China
  11. 61905121/National Natural Science Foundation of China
  12. BK20190734/Natural Science Foundation of Jiangsu Province
  13. NRF-NRFI05-2019-0003/National Research Foundation Singapore

MeSH Term

Artificial Organs
Gallium
Indium
Magnetics
Models, Biological
Neurons
Semiconductors
Synapses
Transistors, Electronic
Zinc Oxide

Chemicals

Indium
Gallium
Zinc Oxide
Journal Article Review

Word Cloud

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