Liu Jinchao, Zhang Di, Yu Dianqiang, Ren Mengxin, Xu Jingjun
The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics Institute, Nankai University, Tianjin, 300071, China.
College of Artificial Intelligence, Nankai University, Tianjin, 300071, China.
Light Sci Appl. 2021 Mar 12;10(1):55. doi: 10.1038/s41377-021-00482-0.
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.
椭圆偏振测量法是一种用于确定薄膜光学常数和厚度的强大方法。几十年来，不适定椭圆偏振逆问题的解决方案需要大量人类专家的干预，并且本质上已成为人工参与的反复试验过程，这不仅繁琐且耗时，还限制了椭圆偏振测量法的适用性。在此，我们展示了一种基于机器学习的方法，该方法能以明确且全自动的方式解决椭圆偏振问题，同时展现出卓越的性能。通过使用涵盖金属、半导体和电介质类别的多种薄膜进行实验验证了所提出的方法。此方法与现有的椭圆偏振仪兼容，为实现薄膜的自动、快速、高通量光学表征铺平了道路。
