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    Area of Science:

    • Optics and Photonics
    • Materials Science
    • Artificial Intelligence

    Background:

    • Metamaterial perfect absorbers (MPAs) are typically designed using deep neural networks (DNNs) that predict structure from absorptivity.
    • This conventional approach faces challenges as the actual optical spectrum is unknown before the metamaterial structure is determined.

    Purpose of the Study:

    • To develop a novel DNN-based method for the reverse design of MPAs.
    • To enable the prediction and design of metamaterial eigenstructures based on designated eigenfrequencies.

    Main Methods:

    • Proposed a metamaterial perfect absorber (MPA) structure utilizing quick response (QR)-code meta-atoms.
    • Developed a novel deep neural network (DNN) capable of predicting and reverse designing eigenstructures by taking designated eigenfrequencies as input.

    Main Results:

    • The proposed DNN successfully predicts and reverse designs metamaterial eigenstructures from specified eigenfrequencies.
    • The QR-code meta-atoms offer numerous degrees of freedom, leading to rich optical properties like multiple absorption peaks.

    Conclusions:

    • This research presents a new paradigm for the eigenproblem study of complex metamaterials and metasurfaces.
    • The developed method facilitates efficient reverse design of MPAs with tailored spectral properties.