A practitioner's approach to evaluation strategy for ellipsometric measurements of multilayered and multiparametric thin-film structures

• An improved approach for multiparametric thin-film stack evaluation is proposed.
• The Akaike and Bayesian information criteria to perform model selection were used.
• The information criteria provide more accurate understanding of model merits.
• The scenario can be accompanied by some kind of post hoc cross-validation method.
• The approach might be useful in many practical cases in the microelectronic industry.

Ellipsometry as an indirect optical measurement method requires the use of optical modelling which include model parameterization. In practice, there are many ways to select a model and its parameters to fit the experimental data. Very often this fact leads to ad hoc decisions, i.e., based on experience or subjective opinion, instead use of some systematic approaches which provide predictive capability. In this paper we use the Akaike and Bayesian information criteria to perform optical model selection and its best parameterization to fit a particular set of ellipsometric data. We demonstrate that this approach accompanied by post hoc study of the inter-parameter correlations can significantly enhance optical modelling, in particularly, the process of model selection and data interpretation and improve the characterization of multilayered thin-film structures.