Experimental demonstration of deep learning enabled ultra-broadband epsilon-near-zero perfect absorbers

David Dang; Sudip J. Gurung; Juan Manuel Raffo Calixto; Xuguo Zhou; Aleksei Anopchenko; Howard Lee

doi:10.1117/12.2678517

5 October 2023 Experimental demonstration of deep learning enabled ultra-broadband epsilon-near-zero perfect absorbers

David Dang, Sudip J. Gurung, Juan Manuel Raffo Calixto, Xuguo Zhou, Aleksei Anopchenko, Howard Lee

Author Affiliations +

Proceedings Volume PC12648, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXI; PC1264807 (2023) https://doi.org/10.1117/12.2678517
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

The design of multilayer ENZ stacks is challenging due to the many parameters involved, including the number of layers, thicknesses, ENZ wavelength, and optical losses. Our machine learning-based approach enables us to efficiently search through the vast design space and experimentally verify the performance of the resulting thin film stack. The resulting 2-layered AZO ENZ thin film stack achieved perfect absorption of light (> 95%) in the near-infrared region from 1500 nm to 2500 nm, highlighting the potential of machine learning techniques in designing ENZ materials for a range of applications.

Conference Presentation

Citation Download Citation

David Dang, Sudip J. Gurung, Juan Manuel Raffo Calixto, Xuguo Zhou, Aleksei Anopchenko, and Howard Lee "Experimental demonstration of deep learning enabled ultra-broadband epsilon-near-zero perfect absorbers", Proc. SPIE PC12648, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXI, PC1264807 (5 October 2023); https://doi.org/10.1117/12.2678517

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