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

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
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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
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