volume | PIER Journals

Abstract

This study investigates the relation between the physical parameters and the scattering parameter (S₁₁) curves of antennas, and proposes two deep-neural-network-based frameworks respectively for antenna forward and inverse designs, improving the design efficiency compared to the conventional electromagnetic (EM) simulation approaches. In this study, a one-dimensional (1D) U-Net is utilized as the backbone of the two models and is enhanced with multiple mechanisms - the diffusion mechanism, channel attention, and spatial attention. Therefore, the models more effectively capture the sequential features of data. In the forward design, the model quickly predicts the S₁₁ curves from given physical parameters with an accuracy improvement of at least 63% RMSE and 70% MAE compared to the improved one-dimensional convolutional neural network (1D-MCNN) and deep multi-layer perceptron (DMLP), thus realizing the surrogate model of conventional methods to some extent. In the inverse design, another model directly infers the physical parameters corresponding to the target S₁₁ curves with an accuracy improvement of at least 21% RMSE and 38% MAE compared to the baseline models (1D U-Net and MLP), thereby eliminating the iterative process of traditional methods and accelerating the antenna design. The experimental results demonstrate the significant advantages of the proposed deep neural network frameworks in terms of accuracy and efficiency for both forward and inverse designs of antennas, offering a powerful alternative to conventional electromagnetic simulation-based approaches.

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Dr. Ximin Yang

Professor Jingchang Nan

Dr. Minghuan Wang