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 GeSbTe (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 GeSbTe 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.
Highlights
Introduction to Data Analysis in SpectroscopicEllipsometryEllipsometry is an optical characterization method that measures the change of polarized light reflected from flat samples
We present the application of deep learning (DL) to analyze spectroscopic ellipsometry data measured on layered (i.e., 2D) materials, and discuss the limitations imposed by materials with
Conventional machine learning based on nonlinear regression may converge reasonably well, but only when the model parameters are initialized well, and this often requires much experience
Summary
Introduction to Data Analysis in SpectroscopicEllipsometryEllipsometry is an optical characterization method that measures the change of polarized light reflected (or transmitted) from flat samples. The three methods introduced earlier can be considered (classified) as a type of conventional machine learning (ML) These traditional ML algorithms train a mathematical model based on experimental data, and the training parameters are the material properties of interest, such as the refractive index. Conventional machine learning based on nonlinear regression may converge reasonably well, but only when the model parameters are initialized well, and this often requires much experience These challenges are well addressed by DL. DL can learn the inverse function on a lower dimensional manifold instead of over the entire parameter space These advantages counter the exponential complexity arising from geometrically large parameter spaces, making DL feasible in many cases for which conventional ML is challenging. Training ANNs may take hours, DL can subsequently process large data sets much faster than
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