Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.
This content will become publicly available on July 1, 2025
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.
more » « less- Award ID(s):
- 2240562
- PAR ID:
- 10538778
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Laser & Photonics Reviews
- Volume:
- 18
- Issue:
- 7
- ISSN:
- 1863-8880
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill-posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics-informed deep learning (DL) framework is used. A tandem DL architecture with physics-based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics-based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.more » « less
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Abstract 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.
