Complex-Amplitude Metasurface Design Assisted by Deep Learning

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.

Automated summary

In this paper, a deep learning scheme is proposed to assist the design of complex-amplitude metasurfaces, making it easier and more efficient. The proposed method shows promising potential for designing more complex-amplitude meta-devices with multi-polarization and multi-wavelength multiplexing capabilities.