Reconfigurable Metasurface Hologram of Dynamic Distance via Deep Learning - Zou, Yijun; Zhu, Rongrong; Shen, Lian; Zheng, Bin (Frontiers in Materials, 2022)
Reconfigurable metasurfaces have been regarded as an emerging subfield of metasurfaces that can manipulate electromagnetic wave information in a smart manner. They stimulate a gradual transition in metasurface holography from passive to active elements. To date, intelligent dynamic holographic imaging schemes typically rely on iterative or data-driven methods to obtain holograms at a fixed imaging distance, which significantly hinders the development of intelligent dynamic holographic imaging in practical scenarios involving high demands for dynamic imaging distances. Herein, a computer-generated hologram algorithm with a dynamic imaging distance and a reconfigurable metasurface are proposed, which is referred to as a neural network incorporating a diffractive model. Simulation results of time–distance division for three-dimensional imaging are provided to demonstrate the reliability and high efficiency of the proposed algorithm.