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This paper describes an inverse analysis method using neural networks onoptical spectroscopy, and its application to the quantitative optical constantevaluation. The present method consists of three subprocesses. First,measurable UV-visible spectroscopic quantities were calculated as functions ofthe optical constants of the solid based on the Tomlin equations [J. Phys. D 11667 (1968)] by carefully eliminating the unpractical combinations of opticalconstants. Second, the back-propagation neural network is trained using thecalculated relationships between the measurable quantities and the opticalconstants. Finally, the trained network is utilized to determine the opticalconstants from measured responses. The conventional (Newton-Raphson) methodtends to require the judgement of a well-experienced analyst, while machinelearning shows automatically human-free performance in data conversion.

Evaluation of optical constants in oxide thin films using machine learning