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Related Experiment Videos

Interpretable-machine-learning-enabled discretized antireflective multilayer design outperforms traditional

Geon-Tae Park, Jinming He, Wenzhuo Zhao

    Applied Optics
    |April 24, 2026
    PubMed
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    We developed a machine learning approach for designing deep-ultraviolet antireflective coatings. Our discrete optimization method achieved significantly lower reflectance than traditional designs, even with fabrication variations.

    Area of Science:

    • Optics and Photonics
    • Materials Science
    • Machine Learning Applications

    Background:

    • Designing deep-ultraviolet (DUV) antireflective coatings is challenging due to limited material properties and sensitivity to thickness errors.
    • Traditional multilayer coatings often struggle to meet stringent performance requirements in the DUV spectrum.

    Purpose of the Study:

    • To develop a novel discrete optimization framework for designing high-performance DUV antireflective coatings.
    • To leverage machine learning, specifically active learning with second-order factorization machines, for inverse design.
    • To improve robustness against fabrication imperfections compared to existing methods.

    Main Methods:

    • Treated antireflective coating design as a discrete optimization problem.

    Related Experiment Videos

  • Employed an active learning scheme with second-order factorization machines using binary, quaternary, and octal refractive index bases.
  • Benchmarked discrete designs against analytical double/tri-layer and idealized graded-index coatings.
  • Assessed fabrication tolerance using Monte Carlo simulations with layer thickness variations.

    • Main Results:

    • The best quaternary design achieved a reflectance of 0.05% at 170 nm, outperforming ideal graded-index profiles.
    • Discrete designs significantly outperformed analytical multilayer coatings.
    • Octal designs demonstrated superior robustness to fabrication perturbations (±50% thickness variation) compared to binary designs, maintaining near 1% reflectance.
    • Phasor-based interpretation explained the effectiveness of factorization machines in navigating the design space.

    Conclusions:

    • A discrete optimization framework using machine learning can surpass traditional analytical multilayer design rules for high-performance coatings.
    • The proposed method enables fast, interpretable, and fabrication-tolerant inverse design of advanced optical coatings.
    • Richer refractive index bases in discrete optimization enhance robustness to manufacturing tolerances.