Quantifying parameter uncertainties in optical scatterometry using Bayesian inversion

TL;DR

This paper introduces a Newton-like Bayesian inversion method for optical scatterometry that quantifies parameter uncertainties, incorporates prior information, and assesses the impact of numerical accuracy on reconstructions.

Contribution

It presents a novel Newton-like approach for Bayesian inverse problems in optical scatterometry, emphasizing uncertainty quantification and numerical accuracy considerations.

Findings

Effective parameter uncertainty quantification in optical scatterometry.

Influence of numerical accuracy on reconstruction results.

Integration of prior information and measurement uncertainties.

Abstract

We present a Newton-like method to solve inverse problems and to quantify parameter uncertainties. We apply the method to parameter reconstruction in optical scatterometry, where we take into account a priori information and measurement uncertainties using a Bayesian approach. Further, we discuss the influence of numerical accuracy on the reconstruction result.