Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning

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Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning
Title:
Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning
Journal Title:
Proceedings of the 47th IEEE Photovoltaic Specialists Conference (PVSC'20), 2020
DOI:
Publication Date:
15 June 2020
Citation:
Abstract:
Thin film processes are ubiquitous in photovoltaics research and are increasingly incorporated into high-throughput experimentation (HTE) equipment. However, the lack of rapid yet accurate thickness measurements limits the throughputs of HTE. This study demonstrates rapid yet accurate thin-film thickness extraction by leveraging machine learning (ML) in combination with a rapid non-destructive optical measurement (UV-Vis). We achieve 86.1% accuracy of thickness prediction within 10- percentage-error bounds on simulated data.
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Funding Info:
The Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043
Description:
“© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
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