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

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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0^{0}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><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.