Using Gaussian process regression for efficient parameter reconstruction
This work addresses efficiency improvements in parameter reconstruction for optical scatterometry, which is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of reconstructing geometry parameters of periodic micro- or nanostructures in optical scatterometry by comparing Bayesian optimization with Gaussian process regression to local minimization algorithms, finding that pre-computed simulations can accelerate the optimization process.
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.