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Using Gaussian process regression for efficient parameter reconstruction
Author(s): Philipp-Immanuel Schneider; Martin Hammerschmidt; Lin Zschiedrich; Sven Burger
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Paper 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.

Paper Details

Date Published: 26 March 2019
PDF: 8 pages
Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095911 (26 March 2019); doi: 10.1117/12.2513268
Show Author Affiliations
Philipp-Immanuel Schneider, JCMwave GmbH (Germany)
Martin Hammerschmidt, JCMwave GmbH (Germany)
Lin Zschiedrich, JCMwave GmbH (Germany)
Sven Burger, JCMwave GmbH (Germany)
Zuse Institute Berlin (Germany)


Published in SPIE Proceedings Vol. 10959:
Metrology, Inspection, and Process Control for Microlithography XXXIII
Vladimir A. Ukraintsev; Ofer Adan, Editor(s)

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