@inbook{1c802e26fce14723b3205576e486244d,
title = "Diffractive characterization of sub-wavelength objects with machine learning",
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.",
author = "Abantika Ghosh and Roth, {Diane J.} and Nicholls, {Luke H.} and Wardley, {William P.} and Anatoly Zayats and Podolskiy, {Viktor A.}",
note = "Funding Information: This research was partially supported by the Army Research Office (US) Grant #W911NF-16-1-0261, National Science Foundation (US) Grant #IIS-2026703, EPSRC (UK), and ERC iCOMM project (789340). Publisher Copyright: {\textcopyright} OSA 2021, {\textcopyright} 2021 The Author(s); CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 ; Conference date: 09-05-2021 Through 14-05-2021",
year = "2021",
language = "English",
series = "Optics InfoBase Conference Papers",
publisher = "The Optical Society",
booktitle = "CLEO",
address = "United States",
}