Model selection in spectroscopic ellipsometry data analysis: Combining an information criteria approach with screening sensitivity analysis

Highlights: • An improved approach to a model-based data analysis in ellipsometry is proposed. • The approach relies on the Akaike and Bayesian information criteria. • The information criteria provide more accurate understanding of models merits. • The screening-type Morris technique based on "elementary effects" was employed. • The effectiveness of the method has been demonstrated by practical example. - Abstract: In the field of optical metrology, the selection of the best model to fit experimental data is absolutely nontrivial problem. In practice, this is a very subjective and formidable task which highly depends on metrology expert opinion. In this paper, we propose a systematic approach to model selection in ellipsometric data analysis. We apply two well-established statistical methods for model selection, namely, the Akaike (AIC) and Bayesian (BIC) Information Criteria, to compare different dispersion models with various complexities and objectively determine the "best" one from a set of candidate models. The information criteria suggest the most optimal way to quantify the balance between goodness of fit and model complexity. In combination with screening-type parametric sensitivity analysis based on so-called "elementary effects" (the Morris method) this approach allows to compare and rate various models, identify key model parameters and significantly enhance process of ellipsometric measurements evaluation.