Fourier Subspace-Based Deep Learning Method for Inverse Design of Frequency Selective Surface

Frequency selective surface (FSS) is critical for electromagnetic (EM) radiation protection due to its high spatial filtering performance, especially for active FSS. Recently, the artificial neural network (ANN) has shown great potential in solving EM inverse problems and rapid industrial design. In such an inverse model with ANN, it establishes the relationship between the given inputs of S-parameters and the desired structure parameters or material parameters. However, faced with applications where S-parameters vary in a large frequency range with different curve shapes, such as multiband microwave devices, equal interval sampling may result in high-dimensional inputs and will require a more complicated neural network. In this work, we present a Fourier subspace-based deep learning method (FS-BDLM) for FSS inverse design, where the dimension of the input is largely reduced by using Fourier subspace to represent the most salient features of the desired S-parameter performance. Compared with existing deep learning methods, the proposed technique makes inverse neural models more compact and more stable to noise contaminations. The validation of the proposed FS-BDLM is conducted both numerically and experimentally through two dual-passband FSS design examples, where the well-designed FSS is fabricated to validate the technique.

Automated summary

This study proposes a Fourier subspace-based deep learning method for inverse design of FSS. By using Fourier subspace to represent the most salient features of the desired S-parameter performance, the method significantly reduces the input dimension and makes the inverse neural models more compact and stable.