  Optical scatterometry is a method to measure the size and shape of periodic
micro- or nanostructures on surfaces. For this purpose the geometry parameters
of the structures are obtained by reproducing experimental measurement results
through numerical simulations. We compare the performance of Bayesian
optimization to different local minimization algorithms for this numerical
optimization problem. Bayesian optimization uses Gaussian-process regression to
find promising parameter values. We examine how pre-computed simulation results
can be used to train the Gaussian process and to accelerate the optimization.
