Machine learning powered ellipsometry

Jinchao Liu; Di Zhang; Dianqiang Yu; Mengxin Ren; Jingjun Xu

doi:10.1038/s41377-021-00482-0

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Machine learning powered ellipsometry

Articles

- Machine learning powered ellipsometry
- Light: Science & Applications Vol. 10, Issue 4, Pages: 582-588(2021)
- Affiliations：
  
  1.The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics Institute, Nankai University, Tianjin, 300071, China
  2.College of Artificial Intelligence, Nankai University, Tianjin, 300071, China
  3.Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi, 030006, China
- Author bio：
  
  Mengxin Ren (ren_mengxin@nankai.edu.cn)
  Jingjun Xu (jjxu@nankai.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00482-0
  CLC：
- Published：30 April 2021，
  
  Published Online：12 March 2021，
  
  Received：18 June 2020，
  
  Revised：30 January 2021，
  
  Accepted：30 January 2021
- Accepted：
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Liu, J. C. et al. Machine learning powered ellipsometry. Light: Science & Applications, 10, 582-588 (2021).
DOI：

Liu, J. C. et al. Machine learning powered ellipsometry. Light: Science & Applications, 10, 582-588 (2021). DOI： 10.1038/s41377-021-00482-0.

摘要

Abstract

Ellipsometry is a powerful method for determining both the optical constants and thickness of thin films. For decades

solutions to ill-posed inverse ellipsometric problems require substantial human-expert intervention and have become essentially human-in-the-loop trial-and-error processes that are not only tedious and time-consuming but also limit the applicability of ellipsometry. Here

we demonstrate a machine learning based approach for solving ellipsometric problems in an unambiguous and fully automatic manner while showing superior performance. The proposed approach is experimentally validated by using a broad range of films covering categories of metals

semiconductors

and dielectrics. This method is compatible with existing ellipsometers and paves the way for realizing the automatic

rapid

high-throughput optical characterization of films.

关键词

Keywords

references

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