Inverse design of electromagnetically induced transparency metamaterials based on generative adversarial networks

Abstract The traditional metamaterial design process usually relies on some knowledge experience and simulation tools to continuously optimize by trial and error, until the simulation results meet the requirements. But this trial-and-error approach could be more unstable and time-consuming, especially when there are too many material parameters or the optimization interval is too large. This paper proposes a multi-prediction model for metamaterials, Improved-StarGan based on StarGan with semi-supervised learning, and use an EIT structure as a validation object. The generator can output various material structures according to the input spectrum extremes, and the discriminator can forward predict the spectrum extremes based on the input material structure parameters. Spectral normalization, gradient penalty, and hidden space distance regularization are also used to increase the diversity of its output data at the expense of sacrificing a part of the accuracy of the generator. During model training, the loss values of the training and validation sets converge normally and end up in a small range. Finally, the data was extracted from the test set for model prediction and simulation comparison. Meanwhile, a sample of one of the predicted structures is tested. All the results show that the model predictions have low error and high confidence. the results demonstrate that the method is effective in both inverse multiple structure and forward prediction of metamaterials, which provides a new design idea for the structural design of metamaterials.