Material Identification by Plasmonic Infrared Microspectrometer Employing Machine Learning

Jiajun Meng; Luke Weston; Sivacarendran Balendhran; Dandan Wen; Jasper J. Cadusch; Ranjith Rajasekharan Unnithan; Kenneth B. Crozier; Kenneth B. Crozier; Kenneth B. Crozier

doi:10.1364/CLEO_SI.2021.SW3B.3

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper SW3B.3
•https://doi.org/10.1364/CLEO_SI.2021.SW3B.3

Material Identification by Plasmonic Infrared Microspectrometer Employing Machine Learning

Jiajun Meng, Luke Weston, Sivacarendran Balendhran, Dandan Wen, Jasper J. Cadusch, Ranjith Rajasekharan Unnithan, and Kenneth B. Crozier

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Novel Device Applications (SW3B)

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J. Meng, L. Weston, S. Balendhran, D. Wen, J. J. Cadusch, R. R. Unnithan, and K. B. Crozier, "Material Identification by Plasmonic Infrared Microspectrometer Employing Machine Learning," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper SW3B.3.

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Abstract

We demonstrate a microspectrometer comprising plasmonic filters integrated with an infrared camera. Blackbody light illuminates the material being studied, with transmitted light collected by the microspectrometer. The latter uses machine learning to identify the material.

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