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

Proceedings of the 47th IEEE Photovoltaic Specialists Conference (PVSC'20), 2020

https://www.ieee-pvsc.org/ePVSC/mobile/show_presentation.php?abstractno=73

Parker Tian Siyu Isaac, Liu Zhe, Chellappan Vijila, Lim Yee-Fun, Ren Zekun, Oviedo Felipe, Teo B. Heng, Thapa Janak, Dutta Rajdeep, MacLeod Benjamin P, Parlane Fraser G. L., Senthilnath J., Berlinguette Curtis P., Buonassisi Tonio

thin film, thickness, Machine Learning, UV-Vis, high-throughput, HTE, refractive index, ellipsometry, optical properties, spectrophotometry, artificial intelligence, autonomous lab

15 June 2020

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.

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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https://oar.a-star.edu.sg/communities-collections/articles/16585

Institute for Infocomm Research