# Using Gaussian process regression for efficient parameter reconstruction

## TL;DR

This paper explores the use of Gaussian process regression within Bayesian optimization to efficiently reconstruct parameters in optical scatterometry, demonstrating accelerated convergence through pre-computed simulation data.

## Contribution

It introduces a method combining Gaussian process regression with Bayesian optimization and pre-computed data to improve parameter reconstruction efficiency in optical scatterometry.

## Key findings

- Bayesian optimization outperforms local minimization algorithms.
- Pre-trained Gaussian processes accelerate the optimization process.
- The approach reduces the number of simulations needed for accurate reconstruction.

## 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.