Deep Learning Approach for the Surface Resistivity of a Multilayer Structure
2026 Symposium on Microwave, Antenna, and Propagation, SMAP 2026, Jakarta, Indonesia, 16 - 17 January 2026, pp.180-185, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/smap67528.2026.11439383
- City: Jakarta
- Country: Indonesia
- Page Numbers: pp.180-185
- Keywords: deep learning, electromagnetic modeling, multilayer structures, Surface resistivity
- Eskisehir Osmangazi University Affiliated: Yes
Abstract
Accurate prediction of surface resistivity is important for analyzing multilayer electromagnetic structures, but classical multilayer solvers become computationally expensive for wide parameter sweeps. This work proposes a deep learning surrogate that predicts the effective surface resistivity from material parameters, layer thicknesses, incidence angle, and frequency. Training data are generated using exact multilayer formulations with the surface transition condition. The resulting neural network achieves high accuracy across diverse configurations, capturing smooth and resonance-dominated responses. The model provides a fast alternative to traditional multilayer solvers for design and optimization tasks.