Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

Abstract

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes, requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device, exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells, photodetectors, and optical filters.