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Deep Learning: A New Tool for Photonic Nanostructure Design

Abstract

Early results have shown the potential of Deep Learning (DL) to disrupt the fields of optical inverse-design, particularly, the inverse design of nanostructures. In the last three years, the complexity of the optical nanostructure design problem being addressed and the sophistication of the employed DL methodology have steadily increased. This topical review comprehensively surveys DL based design examples from the nanophotonics literature. Notwithstanding the early success of this approach, its limitations, range of validity and its place among established design techniques remains to be assessed. The review also provides a perspective on the limitations of this approach and emerging research directions. It is hoped that this topical review may help readers to: identify unaddressed problems; to choose an initial setup for a specific problem; and, to identify means to improve the performance of existing DL based workflows.

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Article information


Submitted
16 Oct 2019
Accepted
11 Feb 2020
First published
12 Feb 2020

This article is Open Access

Nanoscale Adv., 2020, Accepted Manuscript
Article type
Minireview

Deep Learning: A New Tool for Photonic Nanostructure Design

R. Hegde, Nanoscale Adv., 2020, Accepted Manuscript , DOI: 10.1039/C9NA00656G

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    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
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    [Original citation] - Published by The Royal Society of Chemistry.

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