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.

椭圆光度法是确定薄膜光学常数和厚度的有效方法。几十年来，不适定逆椭圆测量问题的解决方案需要大量的人类专家干预，并且基本上已经成为人在循环中的试错过程，这不仅乏味且耗时，而且还限制了椭圆测量的适用性。在这里，我们演示了一种基于机器学习的方法，以明确且全自动的方式解决椭圆测量问题，同时表现出卓越的性能。所提出的方法通过使用涵盖金属、半导体和电介质类别的广泛薄膜进行了实验验证。该方法与现有椭偏仪兼容，为实现薄膜的自动、快速、高通量光学表征铺平了道路。