In order to quantify the uncertainties of reconstructed geometry parameters, a fast-to-evaluate surrogate for the forward model (a polynomial chaos expansion) is introduced. The surrogate allows, e.g., for determining the probability distribution of the geometry paramters given measurement data, and for a global sensitivity analysis of the measurement process. All methods are implemented in JCMsuite's analysis and optimization toolbox.
N. Frachmin, et al. Efficient Bayesian inversion for shape reconstruction of lithography masks. Journal of Micro/Nanolithography, MEMS, and MOEMS, 19(2), 024001 (2020).