Reconfigurable Metasurface Hologram of Dynamic Distance via Deep Learning
Yijun Zou, Rongrong Zhu, Lian Shen, Bin Zheng
Abstract
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 generator and physical diffractive network. Simulation results of time–distance division for three-dimensional imaging are provided to demonstrate the reliability and high efficiency of the proposed algorithm.