Improved Transformer-Based Target Matching of Terahertz Broadband Reflective Metamaterials With Monolayer Graphene

On-demand metamaterial designs aided by artificial intelligence have hitherto received tremendous attention recently. However, the traditional deep neural networks (DNNs) still show the limited generalization ability in the inverse design of tunable graphene-based terahertz (THz) metamaterial. In this article, we propose two kinds of DNNs based on the self-attention mechanism to implement the inverse design of tunable broadband reflectors working in the THz band. Moreover, the proposed networks have been improved, so that they could adapt to different types of input vector or matrix in terms of different kinds of on-demand design requirements. Besides, adaptive batch normalization (BN) layers are introduced in our improved networks to accelerate the converging speed with low computational consumption. It could be shown in experiments that the proposed networks exhibit higher accuracy and faster convergence speed than the traditional neural networks, such as multilayer perceptron (MLP) and convolutional neural network (CNN). Finally, this work may provide a key guide for developing THz metamaterials with 2-D materials employing DNNs based on self-attention mechanism.