Machine learning for semiconductors

Chip Pub Date : 2022-12-01 DOI:10.1016/j.chip.2022.100033
Duan-Yang Liu , Li-Ming Xu , Xu-Min Lin , Xing Wei , Wen-Jie Yu , Yang Wang , Zhong-Ming Wei
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引用次数: 3

Abstract

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.

半导体的机器学习
由于电子产品的标准越来越高,半导体材料科学和半导体制造在过去的几十年里蓬勃发展,在这两个领域积累了大量的数据。如果对这些数据进行有效的分析,将有利于发现新的半导体材料和半导体制造业的发展。幸运的是,机器学习作为一种快速发展的计算机科学工具,有望大大加快数据分析的速度。近年来,在半导体材料和半导体制造领域的机器学习研究有很多报道。本文旨在介绍这些研究进展,并对该领域的研究前景进行展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.80
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0.00%
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