Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films. (arXiv:2207.02209v1 [cs.LG])

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



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