Realizing Multi-Absorption Properties Metamaterial Absorbers by a Dual-Channel Tandem Neural Network.

Deep learning-based research on metamaterial absorbers (MAs) has received increasing attention. However, the problem of homogeneity of structure and material of MAs has constrained their further development. In this paper, we designed MA with a top metal layer consisting of eight rectangular nano-rods, and adjusting their lengths can form various structures. In addition, we formed a material database for constructing MAs with the results of random combinations of eight materials and represented them in a coded manner. Meanwhile, we design MAs with ultra-wideband and dual absorption bandwidths using a dual-channel tandem neural network (DTNN). Compared with the existing methods, our method not only simplifies the steps of selecting materials and structures but also enables the design of MAs with different absorption properties. [ABSTRACT FROM AUTHOR]