Generative Network-Enabled Metasurface Design for Improved Electromagnetic Transmission in Vehicle Low-E Glass

Low-Emissivity (Low-E) glass in modern vehicles, while essential for energy efficiency, impedes electromagnetic transmission crucial for advanced communication systems. This paper presents a novel generative approach for the on-demand inverse design of meta-atoms to enhance signal transmission through automotive Low-E glass. We propose a diffusion-based framework utilizing a modified U-Net architecture with attention mechanisms and conditional information integration. Our method efficiently generates meta-atom designs meeting specific S-parameter requirements, significantly outperforming traditional optimization methods in both accuracy and speed. Experimental results demonstrate the model's capability to produce diverse, free-form metasurface unit cells, achieving a Mean Absolute Error of 0.029 on the test dataset. This approach shows promising potential for rapidly designing metasurfaces that enhance electromagnetic transmission in Low-E glass, addressing a critical challenge in automotive communication and sensor technologies.