Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data

Abstract

High-throughput experimental approaches to rapidly develop new materials require high-throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed-loop, high-throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high-throughput studies of thin films of phase-change materials such as Ge?Sb?Te (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS2. How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of Ge?Sb?Te phase-change alloys containing 177 distinct compositions, and identifying the composition with optimal phase-change figure of merit in only 1.4?s of analysis time.