A hybrid genetic-Levenberg Marquardt algorithm for automated spectrometer design optimization

Abstract

Advancements in computational tools have driven increasingly automated, simulation-centric approaches in the design and optimization of spectroscopic electron-optical systems. These augmented methodologies accelerate the optimization process, and can yield better-performing instruments. While classical gradient-based methods had been explored, modern alternatives such as genetic algorithms have rarely been applied. In this paper, we propose a novel fully-automated hybrid optimization method for use on electron-optical systems. An adaptive switching scheme between a Levenberg-Marquardt and a genetic sub-algorithm enables the simultaneous exploitation of the computational efficiency of the former and the robustness of the latter. The hybrid algorithm is demonstrated on two test examples-the parallel cylindrical mirror analyzer, and the first-order focusing parallel magnetic sector analyzer-and is found to outperform both the Levenberg-Marquardt and genetic algorithms individually. Our work is significant as a versatile tool for parallel energy spectrometer design, and can greatly aid the development of mechanically-complex parallel energy analyzers, which are expected to be of utility to the semiconductor industry in the near future.

Keywords: Computational optimization; Electron optics; Energy analyzer; Genetic algorithm; Hybrid algorithm.