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Tunable Plasmonic Absorption in Metal-Dielectric Multilayers via FDTD Simulations and an Explainable Machine Learning Approach
Emmanuel A. Bamidele
https://arxiv.org/abs/2508.04014

Tunable Plasmonic Absorption in Metal-Dielectric Multilayers via FDTD Simulations and an Explainable Machine Learning Approach
Plasmonic devices, fundamental to modern nanophotonics, exploit resonant interactions between light and free electrons in metals to achieve enhanced light trapping and electromagnetic field confinement. However, modeling their complex, nonlinear optical responses remains computationally intensive. In this work, we combine finite-difference time-domain simulations with machine learning to simulate and predict absorbed power behavior in multilayer plasmonic stacks composed of SiO2, gold, silver, …