Deep-learning-assisted design of multi-degree-of-freedom metamaterial absorber

Abstract

In recent years, the development of metamaterial absorbers (MAs) based on deep learning methods has become a popular research topic. Nevertheless, conventional analytical approaches have limitations, leading to MAs characterized by low-degree-of-freedom cell structures, thereby restricting their absorption characteristics. In this study, we propose a multi-degree-of-freedom MA with multiple variable parameters in terms of structures, materials, and number of layers, which are distinguished by coding. Additionally, deep learning models, including a fully connected neural network, recurrent neural network, AlexNet, and residual neural network, are designed to predict structures and absorption spectra. We compare the prediction accuracy of these four neural network models and identify the optimal network model for spectral prediction and on-demand design. Using trained neural network models, we successfully designed broadband, dual-band, and single-band MAs in on-demand design. Compared with other design methods, this method provides a greater degree of freedom in device design, which allows the design of MAs to have more absorption characteristics