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In optical nano metrology numerical models are used widely for parameterreconstructions. Using the Bayesian target vector optimization method we fit afinite element numerical model to a Grazing Incidence X-Ray fluorescence dataset in order to obtain the geometrical parameters of a nano structured linegrating. Gaussian process, stochastic machine learning surrogate models, weretrained during the reconstruction and afterwards sampled with a Markov chainMonte Carlo sampler to determine the distribution of the reconstructed modelparameters. The numerical discretization parameters of the used finite elementmodel impact the numerical discretization error of the forward model. Weinvestigated the impact of the polynomial order of the finite element ansatzfunctions on the reconstructed parameters as well as on the model parameterdistributions. We showed that such a convergence study allows to determinenumerical parameters which allows for efficient and accurate reconstructionresults.

Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions