Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Sarkar, Sulagna; Ji, Anqi; Jermain, Zachary; Lipton, Robert; Brongersma, Mark; Dayal, Kaushik; Noh, Hae Young

doi:10.1002/adpr.202300158

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Journal Article · Wed Oct 11 00:00:00 EDT 2023 · Advanced Photonics Research

DOI:https://doi.org/10.1002/adpr.202300158· OSTI ID:2067627

^[1]; Ji, Anqi ^[2]; Jermain, Zachary ^[3]; Lipton, Robert ^[3]; Brongersma, Mark ^[4]; Dayal, Kaushik ^[5]; ^[6]

Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA
Department of Electrical Engineering Stanford University 450 Jane Stanford Way Stanford CA 94305 USA
Department of Mathematics Louisiana State University 303 Lockett Hall Baton Rouge LA 70803 USA
Geballe Laboratory for Advanced Materials Stanford University 450 Jane Stanford Way Stanford CA 94305 USA
Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA, Center for Nonlinear Analysis Department of Mathematical Sciences Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA, Department of Mechanical Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA, Pittsburgh Quantum Institute University of Pittsburgh Pittsburgh PA 15260 USA
Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA, Department of Civil and Environmental Engineering Stanford University 450 Jane Stanford Way Stanford CA 94305 USA

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%.

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Sponsoring Organization:: USDOE

OSTI ID:: 2067627

Alternate ID(s):: OSTI ID: 2229769

Journal Information:: Advanced Photonics Research, Journal Name: Advanced Photonics Research Journal Issue: 12 Vol. 4; ISSN 2699-9293

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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