Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
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DOI：
10.1002/adpr.202300158
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Sulagna Sarkar;Anqi Ji;Zachary Jermain;R. Lipton;M. Brongersma;K. Dayal;Hae Young Noh
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文献类型：
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作者：
Sulagna Sarkar;Anqi Ji;Zachary Jermain;R. Lipton;M. Brongersma;K. Dayal;Hae Young Noh
光学超材料通过各种限制和散射过程操纵光，提供独特的优势，如高性能，小形状因子和易于与半导体器件集成。然而，由于计算成本呈指数增长和逆问题的不适定性，为复杂的超材料系统设计具有合适光学响应的超材料表面仍然具有挑战性。为了加快元表面的逆向设计的计算，使用了物理信息深度学习（DL）框架。具有基于物理的学习的串联DL架构用于选择科学上一致、设计预测误差低且光学响应准确重建的设计。作者专注于一个代表性的等离子体激元器件的逆设计，并考虑在可见光范围内的波长的单一波长入射或光谱的光学响应的设计的预测。基于物理学的约束是通过求解简化的均匀化模型的电磁波方程导出的。该模型的收敛精度高达97%的逆设计预测与可见光光谱的光学响应作为输入，高达96%的单波长的光作为输入的光学响应，与光学响应的重建精度为99%。
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%.