WGAN based Inverse Design of Active Dual Band FSS with Switchable Transmission

TL;DR

This paper introduces a WGAN-based inverse design method for switchable dual band FSS, improving design flexibility and reducing time compared to traditional methods.

Contribution

It proposes a novel topology generation strategy combined with a simplified U-Net WGAN for inverse electromagnetic design of FSS.

Findings

WGAN achieved 95.59% training accuracy and 90.84% validation accuracy.

The simplified U-Net attained 98.5% training accuracy and 94.1% validation accuracy.

Generated topologies were validated through simulations and experiments.

Abstract

This letter presents a novel design method for switchable dual band transmissive frequency selective surface (FSS). The proposed FSS possesses characteristics of maintaining passband characteristics at high frequencies, while switching from transmission to reflection at low frequencies with pin diodes states altering. Specifically, we propose a crystal growth-based topology generation strategy, and utilize a simplified U-Net Wasserstein GAN (WGAN) neural network model to establish an inverse mapping model from electromagnetic response to structure topology parameters. The trained WGAN achieves training and validation accuracies of 95.59% and 90.84%, while the simplified U-Net attains training and validation accuracies of 98.5% and 94.1%. Using the trained WGAN. The generated structural topologies were validated through full-wave simulations and experimental measurements. The proposed method enhances the design flexibility and overcomes the time- consuming drawbacks of conventional FSS design.