EMI Shielding With Anisotropic Frequency Selective Surfaces: A Neural Network and Equivalent Circuit Approach

• Published in: IEEE journal on multiscale and multiphysics computational techniques Vol. 10; pp. 94 - 103
• Main Authors: SD, Sairam, Dhamodharan, Sriram Kumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Anisotropic; anisotropic frequency selective surface (AFSS); Artificial neural networks; Capacitance; coupled ring structure (CRS); Deep learning; Electromagnetic scattering; Electromagnetic shielding; Electromagnetic shielding structure (ESS); equivalent circuit model (ECM); Equivalent circuits; Frequency selective surfaces; Incidence angle; Inductance; Integrated circuit modeling; Machine learning; multi-layer perceptron (MLP); Multilayer perceptrons; Multilayers; Neural networks; Optimization; Parameters; Resonant frequencies; shielding effectiveness (SE); Structural rings; Substrates
• ISSN: 2379-8815; 2379-8815
• DOI: 10.1109/JMMCT.2025.3528076

A multi-layer perceptron (MLP) model was applied to electromagnetic shielding to analyze a coupled ring anisotropic frequency selective surface (CRAFSS) using an equivalent circuit model. The shielding structure, based on a single-sided RT 5880 array, features unit elements with dimensions of <inline-formula><tex-math notation="LaTeX">0.55\lambda _{0} \times 0.41\lambda _{0}</tex-math></inline-formula> at the resonant frequency. Various deep neural network (DNN) configurations with hidden layers were tested to achieve optimal results, reaching a minimal mean square error (MSE) of <inline-formula><tex-math notation="LaTeX">1.012 \times 10^{-4}</tex-math></inline-formula>. The MLP was trained using input parameters such as S-parameters, resonant frequency, and shielding effectiveness, with the output being the dimensions of the proposed shielding structure. The dataset, built from capacitance and inductance values, was used for testing, training, and validation within the neural network, eventually employing inverse modeling for output prediction. The structure demonstrated stable bandwidth performance despite changes in the incidence angle of transverse magnetic (TM) and transverse electric (TE) polarizations, shifting from <inline-formula><tex-math notation="LaTeX">\theta</tex-math></inline-formula> = <inline-formula><tex-math notation="LaTeX">0^{0}</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">60^{0}</tex-math></inline-formula>. The anisotropic FSS was developed and evaluated, with deep learning results and electromagnetic (EM) simulations playing a key role in the design process.