  This paper describes an inverse analysis method using neural networks on
optical spectroscopy, and its application to the quantitative optical constant
evaluation. The present method consists of three subprocesses. First,
measurable UV-visible spectroscopic quantities were calculated as functions of
the optical constants of the solid based on the Tomlin equations [J. Phys. D 1
1667 (1968)] by carefully eliminating the unpractical combinations of optical
constants. Second, the back-propagation neural network is trained using the
calculated relationships between the measurable quantities and the optical
constants. Finally, the trained network is utilized to determine the optical
constants from measured responses. The conventional (Newton-Raphson) method
tends to require the judgement of a well-experienced analyst, while machine
learning shows automatically human-free performance in data conversion.
