Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions
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Numerical models are used widely for parameter reconstructions in the field of optical nano metrology. To obtain geometrical parameters of a nano structured line grating, we fit a finite element numerical model to an experimental data set by using the Bayesian target vector optimization method. Gaussian process surrogate models are trained during the reconstruction. Afterwards, we employ a Markov chain Monte Carlo sampler on the surrogate models to determine the full model parameter distribution for the reconstructed model parameters. The choice of numerical discretization parameters, like the polynomial order of the finite element ansatz functions, impacts the numerical discretization error of the forward model. In this study we investigate the impact of numerical discretization parameters of the forward problem on the reconstructed parameters as well as on the model parameter distributions. We show that such a convergence study allows to determine numerical parameters which allow for efficient and accurate reconstruction results.
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