Volume 2 Issue 2
May  2023
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Xue Chen, Bingkun Chen, Pengfei Zhao, Vellaisamy A L Roy, Su-Ting Han, Ye Zhou. Nanowire-based synaptic devices for neuromorphic computing[J]. Materials Futures, 2023, 2(2): 023501. doi: 10.1088/2752-5724/acc678
Citation: Xue Chen, Bingkun Chen, Pengfei Zhao, Vellaisamy A L Roy, Su-Ting Han, Ye Zhou. Nanowire-based synaptic devices for neuromorphic computing[J]. Materials Futures, 2023, 2(2): 023501. doi: 10.1088/2752-5724/acc678
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Nanowire-based synaptic devices for neuromorphic computing

© 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 2, Number 2
  • Received Date: 2023-02-23
  • Accepted Date: 2023-03-21
  • Rev Recd Date: 2023-03-17
  • Publish Date: 2023-05-09
doi: 10.1088/2752-5724/acc678
  • 1.

    Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, People’s Republic of China

  • 2.

    College of Physics and Optoelectronic Engineering, Harbin Engineering University, Harbin 150001, People’s Republic of China

  • 3.

    School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, SAR, People’s Republic of China

  • 4.

    College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, People’s Republic of China

  • 5.

    Institute for Advanced Study, Shenzhen University, Shenzhen 518060, People’s Republic of China

  • Author to whom any correspondence should be addressed.

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    Abstract
  • The traditional von Neumann structure computers cannot meet the demands of high-speed big data processing; therefore, neuromorphic computing has received a lot of interest in recent years. Brain-inspired neuromorphic computing has the advantages of low power consumption, high speed and high accuracy. In human brains, the data transmission and processing are realized through synapses. Artificial synaptic devices can be adopted to mimic the biological synaptic functionalities. Nanowire (NW) is an important building block for nanoelectronics and optoelectronics, and many efforts have been made to promote the application of NW-based synaptic devices for neuromorphic computing. Here, we will introduce the current progress of NW-based synaptic memristors and synaptic transistors. The applications of NW-based synaptic devices for neuromorphic computing will be discussed. The challenges faced by NW-based synaptic devices will be proposed. We hope this perspective will be beneficial for the application of NW-based synaptic devices in neuromorphic systems.
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