Extracting Thin-film Optical Parameters from Spectrophotometric Data by Evolutionary Optimization

Extracting optical parameters from spectrophotometric measurements is a challenging task. In a photometric setup, an unknown thin-film is subjected to an incident light beam for a range of admissible wavelengths, which outputs reflectance and transmittance spectra. The current work attempts to solve an inverse problem of extracting thin-film thickness and complex refractive index from reflectance and transmittance spectra for an incident angle of light. The film thickness is a scalar quantity, and the complex refractive index is composed of real and imaginary parts as functions of wavelengths. We leverage evolutionary optimization techniques to solve the underlying inverse problem, which determines the desired parameters associated with two optical dispersion models: ensemble of Tauc-Lorentz (TL) and ensemble of Gaussian oscillators, such that the generated spectra accurately fit the input data. The optimal parameters involved in the adopted models are determined using efficient evolutionary algorithms (EAs). Numerical results validate the effectiveness of the proposed approach in estimating the optical parameters of interest.