Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration

Conventional design methodologies for Frequency Selective Surfaces (FSSs) are often plagued by challenges such as difficulties in determining unit cell structures, a plethora of optimization parameters, and substantial computational demands. In response, researchers have developed deep learning-based approaches for FSS design, highlighting their advantages in terms of high efficiency and low resource consumption. However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. Building on this foundation, we utilized COMSOL (Version 6.0) and MATLAB (Version R2021b) co-simulations to recollect 6000 sets of FSS data in the millimeter-wave band. Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. The findings of this study provide valuable insights for the integration of deep learning with FSSs, enhancing data utilization efficiency, and further advancing the development of efficient, concise, and universal FSS design methodologies. This advancement extends the scope from solving specific problems to addressing a broader class of issues.