Presentation
3 October 2024 Toward meta-atom library: experimental validation of machine-learning-based Mie-tronics
Author Affiliations +
Abstract
We report an experimental validation of a machine learning-based design method that significantly accelerates the development of all-dielectric complex-shaped meta-atoms supporting specified Mie-type resonances at the desired wavelength, circumventing the conventional time-consuming approaches. We used machine learning to design isolated meta-atoms with specific electric and magnetic responses, verified them within the quasi-normal mode expansion framework, and explored the effects of the substrate and periodic arrangements of such meta-atoms. Since the implemented method allowed for the swift transition from design to fabrication, the optimized meta-atoms were fabricated, and their corresponding scattering spectra were measured using white light spectroscopy, demonstrating an excellent agreement with the theoretical predictions.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Natalia M. Litchinitser, Hooman Barati Sedeh, Renee George, Fangxing Lai, Wenhao Li, Yuruo Zheng, Jiannan Gao, Dmitrii Tsvetkov, Shumin Xiao, and Jingbo Sun "Toward meta-atom library: experimental validation of machine-learning-based Mie-tronics", Proc. SPIE PC13109, Metamaterials, Metadevices, and Metasystems 2024, PC1310909 (3 October 2024); https://doi.org/10.1117/12.3031836
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KEYWORDS
Machine learning

Design

Atmospheric particles

Light scattering

Mie scattering

Aerosols

Spectroscopy

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