Chip (Dec 2022)

Machine learning for semiconductors

  • Duan-Yang Liu,
  • Li-Ming Xu,
  • Xu-Min Lin,
  • Xing Wei,
  • Wen-Jie Yu,
  • Yang Wang,
  • Zhong-Ming Wei

Journal volume & issue
Vol. 1, no. 4
p. 100033

Abstract

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Thanks to the increasingly high standard of electronics, the semiconductor material science and semiconductor manufacturing have been booming in the last few decades, with massive data accumulated in both fields. If analyzed effectively, the data will be conducive to the discovery of new semiconductor materials and the development of semicondulctor manufacturing. Fortunately, machine learning, as a fast-growing tool from computer science, is expected to significantly speed up the data analysis. In recently years, many researches on machine learning study of semiconductor materials and semiconductor manufacturing have been reported. This article is aimed to introduce these progress and present some prospects in this field.