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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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.

Original languageEnglish
Title of host publicationCLEO
Subtitle of host publicationQELS_Fundamental Science, CLEO: QELS 2021
PublisherThe Optical Society
ISBN (Electronic)9781557528209
Publication statusPublished - 2021
EventCLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States
Duration: 9 May 202114 May 2021

Publication series

NameOptics InfoBase Conference Papers

Conference

ConferenceCLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021
Country/TerritoryUnited States
CityVirtual, Online
Period9/05/202114/05/2021

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