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Flexible conformal polarization-sensitive structural color optical switch.

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    This study introduces a fast, AI-driven platform for designing polarization-sensitive color switches. The novel bidirectional deep neural network (Bi-DNN) enables rapid inverse design of metasurfaces for advanced optical displays.

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    Area of Science:

    • Photonics and Materials Science
    • Artificial Intelligence in Optics

    Background:

    • Traditional metasurface structural color design is computationally intensive.
    • Existing optical switching mechanisms lack efficiency and speed.

    Purpose of the Study:

    • To develop a bidirectional deep neural network (Bi-DNN) platform for the inverse design of flexible, polarization-sensitive color switches.
    • To significantly accelerate the design process compared to conventional simulations.

    Main Methods:

    • Implemented a bidirectional deep neural network (Bi-DNN) for simultaneous prediction of structural parameters and output colors.
    • Utilized mechanical strain tuning (-10% to 50%) to control the metasurface's optical properties.
    • Validated performance using finite-difference time-domain (FDTD) simulations for comparison.

    Main Results:

    • Achieved prediction speeds 3 orders of magnitude faster than FDTD (64 ms per sample).
    • Demonstrated a wide color gamut (92% sRGB) with high accuracy (ΔE < 2) under strain.
    • Enabled polarization-switchable color display/hiding with a 17.1 dB extinction ratio.
    • Confirmed angular stability (0°-20° incidence).

    Conclusions:

    • The Bi-DNN platform offers a new paradigm for high-performance optical switches.
    • Advances inverse-design foundations for directional color metasurfaces.
    • Presents applications in next-generation displays, smart wearables, and optical encryption.