Addressing multi-scale challenges in metasurface design with differentiable far-field propagation

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

doi:10.1117/12.3013490

7 June 2024 Addressing multi-scale challenges in metasurface design with differentiable far-field propagation

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

Author Affiliations +

Proceedings Volume 13042, Advanced Optics for Imaging Applications: UV through LWIR IX; 1304205 (2024) https://doi.org/10.1117/12.3013490
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Standard scalar wave propagation techniques, such as Fourier optics, struggle with the multi-scale challenges inherent in the inverse design of optical metasurfaces. Conventional approaches often assume that the source and observation planes are axially aligned and share the same spatial size and discretization. This becomes problematic in the case of metasurface design where often the source and observation planes are on the order of the wavelength. Designing metasurfaces nanometer by nanometer for large-scale applications is computationally prohibitive. Current metasurface inverse design methods generally approximate amplitude and phase under the local phase approximation. However, this is insufficient when considering the intricate interactions among nearest neighbors in a metasurface. A more comprehensive understanding requires the consideration of the complex near electric field, which holds richer information about the metasurface’s physics. Yet, computing the resultant complex field at a far distance from the metasurface is both essential for inverse design and challenging. This work presents an evaluation of three computational approaches, i.e., padded field propagation, shifted field propagation, and propagation by the chirp-z-transform, for the explicit purpose of metasurface inverse design. These techniques are implemented in the Pytorch Lightning deep learning framework facilitating optimization using the backpropagation algorithm.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Marshall B. Lindsay, Scott D. Kovaleski, Andy G. Varner, Charlie Veal, Derek T. Anderson, Stanton R. Price, and Steven R. Price "Addressing multi-scale challenges in metasurface design with differentiable far-field propagation", Proc. SPIE 13042, Advanced Optics for Imaging Applications: UV through LWIR IX, 1304205 (7 June 2024); https://doi.org/10.1117/12.3013490

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