Dual‐band optical collimator based on deep‐learning designed, fabrication‐friendly metasurfaces
Name
10.1515_nanoph-2023-0329.pdf
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3.91 MB
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Checksum (MD5)
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Author(s)
Ueno, Akira
Lin, Hung‐I
Yang, Fan
An, Sensong
Martin‐Monier, Louis
Shalaginov, Mikhail Y
Gu, Tian
Hu, Juejun
Date Issued
July 28, 2023
Journal
Nanophotonics
Publisher
Wiley
Citation
Ueno, A., Lin, H.-I., Yang, F., An, S., Martin-Monier, L., Shalaginov, M.Y., Gu, T. and Hu, J. (2023), Dual-band optical collimator based on deep-learning designed, fabrication-friendly metasurfaces. Nanophotonics, 12: 3491-3499.
Version
Final published version
Abstract
Metasurfaces, which consist of arrays of ultrathin planar nanostructures (also known as “meta-atoms”), offer immense potential for use in high-performance optical devices through the precise manipulation of electromagnetic waves with subwavelength spatial resolution. However, designing meta-atom structures that simultaneously meet multiple functional requirements (e.g., for multiband or multiangle operation) is an arduous task that poses a significant design burden. Therefore, it is essential to establish a robust method for producing intricate meta-atom structures as functional devices. To address this issue, we developed a rapid construction method for a multifunctional and fabrication-friendly meta-atom library using deep neural networks coupled with a meta-atom selector that accounts for realistic fabrication constraints. To validate the proposed method, we successfully applied the approach to experimentally demonstrate a dual-band metasurface collimator based on complex free-form meta-atoms. Our results qualify the proposed method as an efficient and reliable solution for designing complex meta-atom structures in high-performance optical device implementations.
Subjects
deep learning
fabrication tolerance
metasurface
multiband
predictive neural network
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1515/nanoph-2023-0329
