Artificial neural network-based inverse design of metasurface absorber with tunable absorption window

Abstract

Although the development of graphene broadband terahertz (THz) absorbers is relatively mature, studies focusing on modulating the absorption window remain limited. Existing research has mainly concentrated on achieving absorption window switching via the phase transition of materials like vanadium dioxide (VO2). However, this method typically allows for switching within two specific windows, lacking the capacity for arbitrary and flexible regulation. This study introduces a novel approach involving a bilayer graphene metasurface absorber (BGMA) to address this limitation, which allows arbitrary manipulation of the absorption windows. To facilitate rapid and accurate selection of the structural parameters, an artificial neural network (ANN)-based inverse design method is employed, which achieves a tunable absorption bandwidth of 6.70 THz with an error amount to 0.59%. Through in-depth mechanistic analyses, it is demonstrated that the BGMA can achieve tunable absorption between a low-frequency broadband absorption (L-FBA) mode and a high-frequency broadband absorption (H-FBA) mode. This tunability arises from the transition between graphene localized surface plasmon resonance and graphene surface plasmon resonance. Overall, the outcomes of this study underscore the remarkable tunable performance of the BGMA achieved through ANN-based inverse design. This innovation holds significant promise for applications in security detection, and object stealth.