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.
Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, metamolecules consisting of multiple meta‐atoms possess emerging features that the meta‐atoms themselves do not possess. Metasurfaces composed of metamolecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multielement systems impede an effective strategy for the design and optimization of metamolecules. Here, a hybrid artificial‐intelligence‐based framework consolidating compositional pattern‐producing networks and cooperative coevolution to resolve the inverse design of metamolecules in metasurfaces is proposed. The framework breaks the design of the metamolecules into separate designs of meta‐atoms, and independently solves the smaller design tasks of the meta‐atoms through deep learning and evolutionary algorithms. The proposed framework is leveraged to design metallic metamolecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large‐scale metasurfaces in a labor‐saving, systematic manner.
more » « less- PAR ID:
- 10458699
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 32
- Issue:
- 6
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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