Inverse Design of Metamaterials via Deep Learning for Electromagnetically Induced Transparency

Abstract

Abstract The physical limitations of metamaterial structures cannot be solved under the conditions of high time cost and complex algorithms in metamaterial inverse engineering in the past. This paper proposes limiting the value range of metamaterial structural parameters through a single structural parameter acquisition method (SSPAM) for the first time, which will meet the expected values of our predictions and obtain high-quality and effective data in a relatively short time. This is the first attempt to use this method to solve the problem of physical limitations in the inverse design of metamaterials effectively. Furthermore, it is a further improvement of inverse design, enhances the reliability of metamaterial inverse design once again, and realizes the idea of on-demand design. The mean squared error of our best deep learning model is 0.00075 and 0.00026 in the training set and validation set, respectively, and 3.0×10−5 in the test set. We input three specific points of the EIT spectrum into our optimal model to predict the corresponding EIT structural parameters inversely, verified by numerical simulation calculation, and obtained satisfactory results. This work can provide new ideas and methods for the inverse design of metamaterials for other models.