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 remain computationally intensive. In this work, we combine finite-difference time-domain (FDTD) simulations with machine learning (ML) to simulate and predict absorbed power behavior in multilayer plasmonic stacks composed of SiO 2 , gold (Au), silver (Ag), and indium tin oxide (ITO). By varying Au and Ag thicknesses (10–50 nm) across a spectral range of 300–1500 nm, we generate spatial absorption maps and integrated power metrics from full-wave solutions to Maxwell’s equations. A multilayer perceptron models global absorption behavior with a mean absolute error (MAE) of 0.0953, while a convolutional neural network predicts spatial absorption distributions with an MAE of 0.0101. SHapley Additive exPlanations identify plasmonic layer thickness and excitation wavelength as dominant contributors to absorption, which peaks between 450 and 850 nm. Gold demonstrates broader and more sustained absorption compared to silver, although both metals exhibit reduced efficiency outside the resonance window. The integrated FDTD–ML framework accelerates plasmonic design while maintaining physical interpretability and predictive accuracy, enabling efficient exploration of tunable optical responses in multilayer nanophotonic systems for applications in optical sensing, photovoltaics, and device engineering.