Deep learning-based approach for multi-functional fabrication-friendly metasurfaces

Akira Ueno; Hung-I Lin; Fan Yang; Sensong An; Louis Martin-Monier; Mikhail Y. Shalaginov; Tian Gu; Juejun Hu

doi:10.1117/12.3002724

13 March 2024 Deep learning-based approach for multi-functional fabrication-friendly metasurfaces

Akira Ueno, Hung-I Lin, Fan Yang, Sensong An, Louis Martin-Monier, Mikhail Y. Shalaginov, Tian Gu, Juejun Hu

Proceedings Volume 12897, High Contrast Metastructures XIII; 1289708 (2024) https://doi.org/10.1117/12.3002724
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Metasurfaces, comprising arrays of ultrathin and planar nanostructures (termed "meta-atoms"), hold immense potential for high-performance optical devices, enabling the precise control of electromagnetic waves with subwavelength spatial accuracy. However, the design of meta-atom structures that satisfy multiple functional criteria and workability presents a formidable challenge that significantly increases the design complexity. To address this challenge, we developed an expedited process for constructing a versatile, fabrication-friendly meta-atom library. This process utilizes deep neural networks in conjunction with a meta-atom selector, which considers the practical fabrication limitations. To corroborate the effectiveness of our method, we successfully employed it to empirically validate a dual-band metasurface collimator utilizing intricate free-form meta-atoms.

Conference Presentation

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

Citation Download Citation

Akira Ueno, Hung-I Lin, Fan Yang, Sensong An, Louis Martin-Monier, Mikhail Y. Shalaginov, Tian Gu, and Juejun Hu "Deep learning-based approach for multi-functional fabrication-friendly metasurfaces", Proc. SPIE 12897, High Contrast Metastructures XIII, 1289708 (13 March 2024); https://doi.org/10.1117/12.3002724

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