As 2D metamaterials, metasurfaces provide an unprecedented means to manipulate light with the ability to multiplex different functionalities in a single planar device. Currently, most pursuits of multifunctional metasurfaces resort to empirically accommodating more functionalities at the cost of increasing structural complexity, with little effort to investigate the intrinsic restrictions of given meta-atoms and thus the ultimate limits in the design. In this work, it is proposed to embed machine-learning models in both gradient-based and nongradient optimization loops for the automatic implementation of multifunctional metasurfaces. Fundamentally different from the traditional two-step approach that separates phase retrieval and meta-atom structural design, the proposed end-to-end framework facilitates full exploitation of the prescribed design space and pushes the multifunctional design capacity to its physical limit. With a single-layer structure that can be readily fabricated, metasurface focusing lenses and holograms are experimentally demonstrated in the near-infrared region. They show up to eight controllable responses subjected to different combinations of working frequencies and linear polarization states, which are unachievable by the conventional physics-guided approaches. These results manifest the superior capability of the data-driven scheme for photonic design, and will accelerate the development of complex devices and systems for optical display, communication, and computing.
Keywords: machine learning; metamaterials; metasurfaces; photonics.
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