Inverse Design of Electromagnetic‐Induced Transparency Metasurface Using Conditional Generative Adversarial Network

Traditional metasurface inverse design methods suffer from intensive manual modeling, laborious data collection, and extensive numerical calculations, leading to a compromised design efficiency and accuracy. To solve these drawbacks, this paper proposes a metasurface inverse design method using a conditional generative adversarial network (CGAN). A dataset construction framework integrating modeling, simulation, and response processing is established to ease data samples collection and reduce manual workload. Different from conventional parameterized modeling, the metasurface unit patterns are constructed by using a pixel‐based graphical modeling method based on encoding matrices. Furthermore, Gramian Angle Fields (GAFs) are incorporated to explore the latent physical correlations in electromagnetic responses, effectively improving the quality of generated metasurface structure images. By embedding an optimized U‐Net architecture into the CGAN model to suppress gradient vanishing and improve training stability and resolution, high‐precision mapping from target responses to metasurface structures is realized. An electromagnetically induced transparency (EIT) metasurface validates the proposed method. The results demonstrate that the proposed method achieves lower MSE, fewer training epochs, and less training cost. The simulated transmission spectra of the designed metasurface agree well with the target spectra with an average spectral accuracy above 90%, exhibiting promising application potentials for metasurfaces inverse design.