On-demand design of dual-band electromagnetically induced transparency metamaterials based on improved convolutional neural network

Metamaterials (MMs) with dual electromagnetically induced transparency (EIT)-like windows possess more powerful capacity in Terahertz filtering than their single-EIT counterparts, but the design difficulty normally increases as the MM structure becomes more complicated. This work proposes a deep-learning method using a long short-term memory (LSTM) assisted convolutional neural network (LSTM-CNN) to simplify the design of dual-EIT MMs. Compared to the traditional CNN, the LSTM-CNN exhibits higher prediction accuracy for the geometric parameters of the MM according to the input spectrum, which can be ascribed to enhanced capability of extracting the spectral characteristics after adding the LSTM network. Further adding a feature-transforming network before the LSTM-CNN model to build a link between the MM performance and structure, a customizable MM design is realized. Two different dual-EIT metamaterials are quickly designed with this strategy, and the design accuracy has been fully demonstrated by simulations and experiments. The proposed deep learning method can achieve the rapid and efficient design of dual-EIT MMs, which offers a new pathway for the ondemand design of complex MM-based devices.