Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films

• URL: http://arxiv.org/abs/2207.02209v1
• Date: Tue, 14 Jun 2022 16:26:15 GMT
• Title: Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films
• Authors: Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
• Abstract summary: thicknessML rapidly extracts film thickness from spectroscopic reflection and transmission. We demonstrate that thicknessML can extract film thickness from six perovskite samples in a two-stage process. Results show a pre-training thickness mean absolute percentage error (MAPE) of 5-7% and an experimental thickness MAPE of 6-19%.
• Score: 11.894117300649372
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-throughput experimentation with autonomous workflows, increasingly used to screen and optimize optoelectronic thin films, requires matching throughput of downstream characterizations. Despite being essential, thickness characterization lags in throughput. Although optical spectroscopic methods, e.g., spectrophotometry, provide quick measurements, a critical bottleneck is the ensuing manual fitting of optical oscillation models to the measured reflection and transmission. This study presents a machine-learning (ML) framework called thicknessML, which rapidly extracts film thickness from spectroscopic reflection and transmission. thicknessML leverages transfer learning to generalize to materials of different underlying optical oscillator models (i.e., different material classes).We demonstrate that thicknessML can extract film thickness from six perovskite samples in a two-stage process: (1) pre-training on a generic simulated dataset of Tauc-Lorentz oscillator, and (2) transfer learning to a simulated perovskite dataset of several literature perovskite refractive indices. Results show a pre-training thickness mean absolute percentage error (MAPE) of 5-7% and an experimental thickness MAPE of 6-19%.

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