We will investigate the problem of transparent electromagnetic interference shielding to protect electronic circuits or devices by finding an optimal nano-structure using Bayesian optimization. We will parameterize a thin-film structure considering the material and thickness of each layer, and then optimize two objective functions with mulit-objective Bayesian optimization. In addition, we will showcase our own transfer-matrix method package for computing the propagation of electromagnetic waves as well as our Bayesian optimization package.

References:

1. Mingxuan Li, Michael J. McCourt, Anthony J. Galante, and Paul W. Leu. Bayesian optimization of nanophotonic electromagnetic shielding with very high visible transparency. Optics Express, vol. 30, no. 18, pp. 33182-33194, 2022.
2. Jungtaek Kim and Seungjin Choi. BayesO: A Bayesian optimization framework in Python. Journal of Open Source Software, vol. 8, no. 90, p. 5320, 2023.
3. Jungtaek Kim, Mingxuan Li, Oliver Hinder, and Paul W. Leu. Datasets and benchmarks for nanophotonic structure and parametric design simulations. In Advances in Neural Information Processing Systems 36 (NeurIPS-2023), New Orleans, Louisiana, USA, December 10-16, 2023. Datasets and Benchmarks Track.
4. Jungtaek Kim, Mingxuan Li, Yirong Li, Andrés Gómez, Oliver Hinder, and Paul W. Leu. Multi-BOWS: Multi-fidelity multi-objective Bayesian optimization with warm starts for nanophotonic structure design. Digital Discovery, vol. 3, no. 2, pp. 381-391, 2024.