Deep neural network-enabled bifunctional terahertz metasurface design for absorption and polarization conversion
Yisong Lv, Shujie Liu, Jinping Tian, Chongrong Mou
Abstract
With the assistance of deep neural networks, a bifunctional metasurface (MS) was designed and optimized, i.e., a broadband absorber and a broadband polarization converter. The MS acts as a wide absorber when the vanadium dioxide (VO2) is in the metallic state and has an absorption bandwidth of 5.21 THz with an absorption rate ≥ 90%. In contrast, the MS acts as a linear–linear polarization converter when the top VO2 is in the insulating state and has a bandwidth of 3.7 THz with a conversion efficiency ≥ 90%. The bandwidth in both states is maximum compared to other bifunctional counterparts, while this bifunctional MS has good parametric and angular tolerance characteristics and low material cost. The proposed structure and design method of the bifunctional MS can provide a useful reference for the research of new multifunctional terahertz devices.