On-demand design based on deep learning and phase manipulation of all-silicon terahertz chiral metasurfaces

Abstract

Chiral metasurfaces have been widely used in sensing, imaging and other fields because they can manipulate light through the efficient circular dichroism (CD). However, its on-demand design is still a very challenging task. In this work, we propose an on-demand multiple reverse design based on deep learning, named target-driven conditional generative network (TCGN). It can reverse design the metasurface structure that meets the required CD, and its mean square error (MAE) is 0.0089. We use this method to inversely design multiple sets of metasurfaces with different structures, and all their CD values can exceed 0.36. Both simulations and experiments prove the feasibility and effectiveness of using deep learning to reverse design metasurfaces. In addition, the designed metasurface can realize chiral wavefront control under dual frequency. This design method based on deep learning can rapidly and efficiently design the chiral metasurfaces, which provides a new way for the design of metasurfaces.