Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile

Rudis Ismael Salinas; Po-Chuan Chen; Chao-Yao Yang; Chih-Huang Lai

doi:10.1080/21663831.2022.2147803

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Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile

Brief Overview | 更新时间：2026-08-19

- Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile
- Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile
- 材料研究快报（英文） 2023年11卷第5期页码：305-326
- 作者机构：
  
  1. a Department of Materials Science and Engineering National Tsing Hua University
  2. b Department of Materials Science and Engineering National Yang Ming Chiao Tung University
- 作者简介：
- 基金信息：
- DOI：10.1080/21663831.2022.2147803
  中图分类号：
- 网络首发：2022-12-05，
  
  纸质出版：2023
- 稿件说明：
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Rudis Ismael Salinas, Po-Chuan Chen, Chao-Yao Yang, 等. Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile[J]. 材料研究快报（英文）, 2023,11(5):305-326.

Rudis Ismael Salinas, Po-Chuan Chen, Chao-Yao Yang, et al. Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile[J]. Materials Research Letters, 2023, 11(5): 305-326.
Rudis Ismael Salinas, Po-Chuan Chen, Chao-Yao Yang, 等. Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile[J]. 材料研究快报（英文）, 2023,11(5):305-326. DOI： 10.1080/21663831.2022.2147803.

Rudis Ismael Salinas, Po-Chuan Chen, Chao-Yao Yang, et al. Spintronic materials and devices towards an artificial neural network: accomplishments and the last mile[J]. Materials Research Letters, 2023, 11(5): 305-326. DOI： 10.1080/21663831.2022.2147803.

摘要

Abstract

BSTRACTNeuromorphic computing based on brain-inspired frameworks has shown its potential to perform computation with remarkable power and speed efficiency. The spintronic device has been considered a promising candidate for neuromorphic computing because of its intrinsic non-volatility

ultrafast switching dynamics and scalability. This article will review the current status of neuromorphic computing techniques employing spintronic materials and devices. We will begin with a concise introduction to the biological structure of neurons and then go through several important models of artificial neural networks. The revolutionary breakthroughs of spintronic-based neuromorphic computation will be demonstrated together with their challenges and outlook.

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