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