Mapping meta-atoms to near field response for inverse design of optical metasurfaces

Andy G. Varner; Marshall B. Lindsay; Charlie T. Veal; Scott D. Kovaleski; Derek T. Anderson; Stanton R. Price; Steven R. Price

doi:10.1117/12.2661887

14 June 2023 Mapping meta-atoms to near field response for inverse design of optical metasurfaces

Andy G. Varner, Marshall B. Lindsay, Charlie T. Veal, Scott D. Kovaleski, Derek T. Anderson, Stanton R. Price, Steven R. Price

Author Affiliations +

Proceedings Volume 12530, Advanced Optics for Imaging Applications: UV through LWIR VIII; 125300J (2023) https://doi.org/10.1117/12.2661887
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Optical metasurfaces enable devices to interact with light in unique ways by modulating phase, polarization, or intensity. A metasurface, composed of individual subwavelength scatterers known as meta-atoms, can be designed to provide unparalleled control of wavefronts for a variety of optical applications, yet the design of such devices is often unintuitive and challenging due to computationally expensive forward simulations and the number of free parameters. To overcome this, there is interest in developing inverse design methods as an alternative to conventional forward design. Inverse design leverages machine learning algorithms to effectively search a problem space, starting from application and resulting in solution parameters. In this work, we adopt an inverse design approach that involves targeted forward simulations of arbitrary meta-atoms. To ensure that the dataset captures all possible shapes and rotations of near field responses with second order accuracy, it is constructed using meta-atoms with varying geometries and corresponding phase shifts, including the effect of nearest neighbors. A custom deep learning system is developed to extract meaningful features from this near field response. The proposed framework provides flexibility to produce an inverse design paradigm for generalized metasurface applications without the need for repeated forward simulations. Additionally, the machine learning model is highly effective in reconstructing electric fields, irrespective of the loss function used.

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Citation Download Citation

Andy G. Varner, Marshall B. Lindsay, Charlie T. Veal, Scott D. Kovaleski, Derek T. Anderson, Stanton R. Price, and Steven R. Price "Mapping meta-atoms to near field response for inverse design of optical metasurfaces", Proc. SPIE 12530, Advanced Optics for Imaging Applications: UV through LWIR VIII, 125300J (14 June 2023); https://doi.org/10.1117/12.2661887

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