Combination of a global-search method with model selection criteria for the ellipsometric data evaluation of DLC coatings

Abstract

Abstract A method for the evaluation of experimental data from spectroscopic ellipsometry is proposed which combines the global-search optimization algorithm with statistical model selection criteria. The hybrid genetic-gradient search algorithm (HGGA) is applied to find the optical parameters and thickness of a diamond-like carbon (DLC) coating deposited on SW7M stainless steel. Akaike and Bayesian information criteria are used to evaluate the different dielectric function models. The method is able to find optical model parameters even in case of a limited initial knowledge about the material optical constants. At the same time, the optimal dielectric function model for the description of the material optical properties can be selected unambiguously from the set of candidate models.