Diffractive Characterization of Sub-wavelength Objects with Machine Learning

Abantika Ghosh; Diane J. Roth; Luke H. Nicholls; William P. Wardley; Anatoly Zayats; Viktor A. Podolskiy

doi:10.1364/CLEO_QELS.2021.FTh2M.6

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper FTh2M.6
•https://doi.org/10.1364/CLEO_QELS.2021.FTh2M.6

Diffractive Characterization of Sub-wavelength Objects with Machine Learning

Abantika Ghosh, Diane J. Roth, Luke H. Nicholls, William P. Wardley, Anatoly Zayats, and Viktor A. Podolskiy

CLEO: QELS_Fundamental Science 2021

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

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A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. Zayats, and V. A. Podolskiy, "Diffractive Characterization of Sub-wavelength Objects with 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 FTh2M.6.

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Abstract

We analyze the limits of a novel machine-learning based technique for characterization of sub-wavelength objects based on their diffractive signatures, achieving theoretical resolution of ~wavelength/25. Experimentally, we demonstrate characterization of 120-nm objects with 850-nm light.

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