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Related Experiment Video

Updated: Nov 14, 2025

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

Published on: January 26, 2016

Machine learning powered ellipsometry.

Jinchao Liu^1,2, Di Zhang¹, Dianqiang Yu¹

¹The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics Institute, Nankai University, Tianjin, 300071, China.

Light, Science & Applications

|March 12, 2021

Summary

This summary is machine-generated.

A new machine learning approach automates ellipsometry, overcoming tedious human-in-the-loop methods for optical characterization of thin films. This breakthrough enables rapid, high-throughput analysis of metals, semiconductors, and dielectrics.

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Area of Science:

Materials Science
Optics
Data Science

Background:

Ellipsometry is crucial for thin film analysis, determining optical constants and thickness.
Traditional inverse problems are ill-posed, requiring extensive human intervention and trial-and-error.
This limits the speed and broad applicability of ellipsometric techniques.

Purpose of the Study:

To develop a machine learning (ML) based approach for solving inverse ellipsometric problems.
To achieve unambiguous and fully automatic optical characterization of thin films.
To demonstrate superior performance compared to existing methods.

Main Methods:

Implementation of a novel machine learning algorithm tailored for ellipsometric data.
Experimental validation using diverse thin film samples: metals, semiconductors, and dielectrics.
Integration compatibility with existing ellipsometer hardware.

Main Results:

The ML approach successfully solved inverse ellipsometric problems automatically and unambiguously.
Demonstrated superior performance and accuracy in optical constant and thickness determination.
Experimental validation confirmed the method's effectiveness across various material types.

Conclusions:

The developed ML method offers a significant advancement in thin film optical characterization.
It eliminates the need for human-in-the-loop processes, saving time and effort.
Enables rapid, high-throughput, and automated optical analysis, expanding ellipsometry's utility.