Journal of Inorganic Materials, Volume. 39, Issue 4, 345(2024)

Oxide Neuron Devices and Their Applications in Artificial Neural Networks

Zongxiao LI1... Lingxiang HU1, Jingrui WANG2, and Fei ZHUGE1,3,45,* |Show fewer author(s)
Author Affiliations
  • 11. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
  • 22. School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
  • 33. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
  • 44. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China
  • 55. Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
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    Nowadays, artificial intelligence (AI) is playing an increasingly important role in human society. Running AI algorithms represented by deep learning places great demands on computational power of hardware. However, with Moore's Law approaching physical limitations, the traditional Von Neumann computing architecture cannot meet the urgent demand for promoting hardware computational power. The brain-inspired neuromorphic computing (NC) employing an integrated processing-memory architecture is expected to provide an important hardware basis for developing novel AI technologies with low energy consumption and high computational power. Under this conception, artificial neurons and synapses, as the core components of NC systems, have become a research hotspot. This paper aims to provide a comprehensive review on the development of oxide neuron devices. Firstly, several mathematical models of neurons are described. Then, recent progress of Hodgkin-Huxley neurons, leaky integrate-and-fire neurons and oscillatory neurons based on oxide electronic devices is introduced in detail. The effects of device structures and working mechanisms on neuronal performance are systematically analyzed. Next, the hardware implementation of spiking neural networks and oscillatory neural networks based on oxide artificial neurons is demonstrated. Finally, the challenges of oxide neuron devices, arrays and networks, as well as prospect for their applications are pointed out.

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    Zongxiao LI, Lingxiang HU, Jingrui WANG, Fei ZHUGE. Oxide Neuron Devices and Their Applications in Artificial Neural Networks[J]. Journal of Inorganic Materials, 2024, 39(4): 345

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    Paper Information

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    Received: Sep. 5, 2023

    Accepted: --

    Published Online: Jul. 8, 2024

    The Author Email: ZHUGE Fei (zhugefei@nimte.ac.cn)

    DOI:10.15541/jim20230405

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