Cheong Kang Hao, Koh Jin Ming
Science and Math Cluster, Singapore University of Technology and Design, 8 Somapah Road, S487372, Singapore; Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, S138683, Singapore.
Science and Math Cluster, Singapore University of Technology and Design, 8 Somapah Road, S487372, Singapore.
Ultramicroscopy. 2019 Jul;202:100-106. doi: 10.1016/j.ultramic.2019.03.004. Epub 2019 Mar 15.
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.
计算工具的进步推动了光谱电子光学系统设计和优化中日益自动化、以模拟为中心的方法。这些增强方法加速了优化过程，并能产生性能更优的仪器。虽然已经探索了基于经典梯度的方法，但诸如遗传算法等现代替代方法很少被应用。在本文中，我们提出了一种用于电子光学系统的新型全自动混合优化方法。一种在Levenberg-Marquardt算法和遗传子算法之间的自适应切换方案能够同时利用前者的计算效率和后者的稳健性。该混合算法在两个测试示例——平行圆柱镜分析器和一阶聚焦平行磁扇形分析器上进行了演示，结果发现其性能优于单独使用的Levenberg-Marquardt算法和遗传算法。我们的工作作为一种用于平行能量谱仪设计的通用工具具有重要意义，并且能够极大地助力机械结构复杂的平行能量分析器的开发，预计在不久的将来对半导体行业具有实用价值。
