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26 March 2019 Using Gaussian process regression for efficient parameter reconstruction
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Abstract
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.
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Philipp-Immanuel Schneider, Martin Hammerschmidt, Lin Zschiedrich, and Sven Burger "Using Gaussian process regression for efficient parameter reconstruction", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095911 (26 March 2019); https://doi.org/10.1117/12.2513268
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