CPOpt: A modular framework for genetic algorithm optimization and post-optimization analysis in complex charged particle optical design

This article reports on the development of an optimization framework for charged particle optics design (CPOpt). The optimization part of CPOpt involves a custom-developed Python multi-objective genetic algorithm (MOGA), which incorporates NSGA-II to optimize for multiple objectives, such as particle transmission and beam spot size. The simultaneous tuning of different types of parameters, such as electrode voltages and geometrical parameters, is facilitated by creating isolated groups of parameters. Modeling of parameter configurations is done with the charged particle optics simulation tool SIMION and a ZeroMQ-based communication interface enables a fully automated interaction with the MOGA. The post-optimization analysis part of CPOpt provides standardized and broadly applicable methods to identify suitable solutions, based on the outcome of the optimization part. Global sensitivity analysis, based on the Delta Moment-Independent-Measure in combination with dedicated Latin hypercube sampling, allows higher-level information about parameter sensitivity and robustness to be extracted, as well as effective randomized local optimization. Demonstrated on a two-lens system with intermediate aperture, the CPOpt framework effectively optimized the two conflicting objectives of reduced beam spot size and increased transmission at the detector plane for 12 parameters, covering both electrode voltages and geometrical parameters. The optimization process was performed within less than 11h on a workstation, while all the simulations required for the post-optimization analysis took another 7h. The additional computational effort for the post-optimization analysis can contribute to deliver solutions with high performance and stability.