Deep-Learning-Empowered Holographic Metasurface with Simultaneously Customized Phase and Amplitude

Metasurfaces with simultaneously and independently controllable amplitude and phase have provided a higher degree of freedom in manipulating electromagnetic (EM) waves. Compared with phase- or amplitude-only modulation, the capability of simultaneously controlling the phase and amplitude of EM waves can enable holography with a higher resolution. However, this drastically increases the design complexity of holographic metasurfaces, and the design process is usually quite time-consuming. In this paper, we propose an inverse design of meta-atoms that can simultaneously and independently tailor the phase and amplitude of transmitted waves using customized deep ResNet while eliminating the coupling of parameters. To demonstrate the design method, two holographic metasurfaces were designed using the trained network without the need for parameter sweeping, which will significantly enhance design efficiency. Prototypes were fabricated and measured. Both the simulated and measured results show that high-resolution holography is obtained, which sufficiently verifies the reliability of the design method. Our work paves the way for the intelligent design of metasurfaces and can also be applied to the design of other artificial materials or surfaces.