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Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances

Publication ,  Journal Article
Li, W; Barati Sedeh, H; Tsvetkov, D; Padilla, WJ; Ren, S; Malof, J; Litchinitser, NM
Published in: Laser and Photonics Reviews
July 1, 2024

In the rapidly developing field of nanophotonics, machine learning (ML) methods facilitate the multi-parameter optimization processes and serve as a valuable technique in tackling inverse design challenges by predicting nanostructure designs that satisfy specific optical property criteria. However, while considerable efforts have been devoted to applying ML for designing the overall spectral response of photonic nanostructures, often without elucidating the underlying physical mechanisms, physics-based models remain largely unexplored. Here, physics-empowered forward and inverse ML models to design dielectric meta-atoms with controlled multipolar responses are introduced. By utilizing the multipole expansion theory, the forward model efficiently predicts the scattering response of meta-atoms with diverse shapes and the inverse model designs meta-atoms that possess the desired multipole resonances. Implementing the inverse design model, uniquely shaped meta-atoms with enhanced higher-order magnetic resonances and those supporting a super-scattering regime of light-matter interactions resulting in nearly five-fold enhancement of scattering beyond the single-channel limit are designed. Finally, an ML model to predict the wavelength-dependent electric field distribution inside and near the meta-atom is developed. The proposed ML based models will likely facilitate uncovering new regimes of linear and nonlinear light-matter interaction at the nanoscale as well as a versatile toolkit for nanophotonic design.

Duke Scholars

Published In

Laser and Photonics Reviews

DOI

EISSN

1863-8899

ISSN

1863-8880

Publication Date

July 1, 2024

Volume

18

Issue

7

Related Subject Headings

  • Optoelectronics & Photonics
  • 5108 Quantum physics
  • 5102 Atomic, molecular and optical physics
  • 0206 Quantum Physics
  • 0205 Optical Physics
  • 0201 Astronomical and Space Sciences
 

Citation

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MLA
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Li, W., Barati Sedeh, H., Tsvetkov, D., Padilla, W. J., Ren, S., Malof, J., & Litchinitser, N. M. (2024). Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances. Laser and Photonics Reviews, 18(7). https://doi.org/10.1002/lpor.202300855
Li, W., H. Barati Sedeh, D. Tsvetkov, W. J. Padilla, S. Ren, J. Malof, and N. M. Litchinitser. “Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances.” Laser and Photonics Reviews 18, no. 7 (July 1, 2024). https://doi.org/10.1002/lpor.202300855.
Li W, Barati Sedeh H, Tsvetkov D, Padilla WJ, Ren S, Malof J, et al. Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances. Laser and Photonics Reviews. 2024 Jul 1;18(7).
Li, W., et al. “Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances.” Laser and Photonics Reviews, vol. 18, no. 7, July 2024. Scopus, doi:10.1002/lpor.202300855.
Li W, Barati Sedeh H, Tsvetkov D, Padilla WJ, Ren S, Malof J, Litchinitser NM. Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances. Laser and Photonics Reviews. 2024 Jul 1;18(7).
Journal cover image

Published In

Laser and Photonics Reviews

DOI

EISSN

1863-8899

ISSN

1863-8880

Publication Date

July 1, 2024

Volume

18

Issue

7

Related Subject Headings

  • Optoelectronics & Photonics
  • 5108 Quantum physics
  • 5102 Atomic, molecular and optical physics
  • 0206 Quantum Physics
  • 0205 Optical Physics
  • 0201 Astronomical and Space Sciences