Using Gaussian process regression for efficient parameter reconstruction

Philipp-Immanuel Schneider; Martin Hammerschmidt; Lin Zschiedrich; Sven Burger

doi:10.1117/12.2513268

26 March 2019 Using Gaussian process regression for efficient parameter reconstruction

Philipp-Immanuel Schneider, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger

Proceedings Volume 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII; 1095911 (2019) https://doi.org/10.1117/12.2513268
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available