Displaying items by tag: software benchmarks
This blog post is based on the publication P.-I. Schneider, et al. Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction.ACS Photonics 6, 2726 (2019). Several global optimization methods for three typical nano-optical optimization problems are benchmarked: particle swarm optimization, differential evolution, and Bayesian optimization as well as multistart versions of downhill simplex optimization and the limited-memory Broydenu2013Fletcheru2013Goldfarbu2013Shanno (L-BFGS) algorithm. In the shown examples, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.
This blog post is based on the publication P.-I. Schneider, et al. Using Gaussian process regression for efficient parameter reconstruction.Proc. SPIE 10959, 1095911 (2019). 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. The performance of Bayesian optimization as implemented in JCMsuite`s optimization toolbox is compared to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. The paper examines how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.
JCMsuite employs the finite element method (FEM) in order to simulate the electrodynamic, mechanic and thermodynamic behavior of nano-optical systems. In this blog we want to motivate and describe the usage FEM for determining the electrodynamic system properties. We will compare the method to other common approaches like the rigorous coupled-wave analysis (RCWA) and the finite difference time domain (FDTD). Finally, we will present some benchmarks to show that FEM can be orders of magnitude faster and more precise than alternative methods.