Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data
Name
Advanced Photonics Research - 2021 - Li - Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data.pdf
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Name
Deep Learning for Ellipsometry v8.3_YL.pdf
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1.14 MB
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Adobe PDF
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Author(s)
Li, Yifei
Wu, Yifeng
Yu, Heshan
Takeuchi, Ichiro
Jaramillo, Rafael
Date Issued
September 23, 2021
Journal
Advanced Photonics Research
Publisher
Wiley
Citation
Li, Yifei, Wu, Yifeng, Yu, Heshan, Takeuchi, Ichiro and Jaramillo, Rafael. 2021. "Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data." Advanced Photonics Research, 2 (12).
Version
Final published version
Abstract
High-throughput experimental approaches to rapidly develop new materialsrequire high-throughput data analysis methods to match. Spectroscopic ellips-ometry is a powerful method of optical properties characterization, but forunknown materials and/or layer structures the data analysis using traditionalmethods 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 deeplearning are introduced and studied. After initial training, the incremental time forinferring optical properties can be a thousand times faster than traditionalmethods. Results for multilayer sample structures with optically isotropicmaterials are presented, appropriate for high-throughput studies of thinfilms ofphase-change materials such as Ge─Sb─Te (GST) alloys. Results for studies onhighly birefringent layered materials are also presented, exemplified by thetransition metal dichalcogenide MoS2. How the materials under test and theexperimental objectives may guide the choice of analysis methods are discussed.The utility of our approach is demonstrated by analyzing data measured on acomposition spread of Ge─Sb─Te phase-change alloys containing 177 distinctcompositions, and identifying the composition with optimal phase-changefigureof merit in only 1.4 s of analysis time.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/adpr.202100147
