标题： 使用高斯过程回归进行高效的参数重构 显示英文标题

标题： Using Gaussian process regression for efficient parameter reconstruction

摘要： 光学散射测量是一种用于测量表面上周期性微米或纳米结构尺寸和形状的方法。 为此，通过数值模拟再现实验测量结果来获得结构的几何参数。 我们比较了贝叶斯优化在这一数值优化问题中与不同局部最小化算法的性能。 贝叶斯优化使用高斯过程回归来寻找有前景的参数值。 我们研究了如何利用预先计算的模拟结果来训练高斯过程并加速优化。 显示英文摘要

摘要： 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.