Complex‐Amplitude Metasurface Design Assisted by Deep Learning

Abstract Metasurfaces can enable powerful manipulations for electromagnetic waves, thus many exotic functionalities have been realized. However, constrained by the complexity of metasurface‐modulated complex amplitudes (amplitude and phase), the design of complex‐amplitude metasurfaces sets a high threshold for researchers because of the requirement of plenty of specialized knowledge. In this paper, a deep learning scheme that uses a forward surrogate network to assist complex‐amplitude metasurface design is proposed. The model is simple to construct and easy to converge in training. Accordingly, two complex‐amplitude multiplexing devices, which can simultaneously display a nanoprinting image at the device surface and one/two holographic images in the far field, are designed with the proposed network using the cross‐shaped meta‐atom. The results show that the demonstrated scheme can be used to design the complex‐amplitude metasurface devices easily and effectively once training of the network is completed, and the single‐layer smooth structure holds the advantage for the fabrication. The proposed method here is promising to realize designs of more complex‐amplitude meta‐devices, multi‐polarization, and multi‐wavelength multiplexing meta‐devices.