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Deep learning meets nanophotonics: A generalized Accurate predictor for near fields and far fields of arbitrary 3D nanostructures

Deep learning meets nanophotonics: A generalized Accurate predictor for near fields and far fields of arbitrary 3D nanostructures
Deep learning meets nanophotonics: A generalized Accurate predictor for near fields and far fields of arbitrary 3D nanostructures
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.
1530-6984
329-338
Wiecha, Peter R.
fb335482-9577-41af-a0ef-3988b7654c9b
Muskens, Otto L.
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Wiecha, Peter R.
fb335482-9577-41af-a0ef-3988b7654c9b
Muskens, Otto L.
2284101a-f9ef-4d79-8951-a6cda5bfc7f9

Wiecha, Peter R. and Muskens, Otto L. (2020) Deep learning meets nanophotonics: A generalized Accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Letters, 20 (1), 329-338. (doi:10.1021/acs.nanolett.9b03971).

Record type: Article

Abstract

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.

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WiechaNanoLett2020 - Accepted Manuscript
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e-pub ahead of print date: 11 December 2019
Published date: 8 January 2020

Identifiers

Local EPrints ID: 437216
URI: http://eprints.soton.ac.uk/id/eprint/437216
ISSN: 1530-6984
PURE UUID: 9e907ccc-eaf3-4ba3-ae47-94ac469bf1f0
ORCID for Otto L. Muskens: ORCID iD orcid.org/0000-0003-0693-5504

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Date deposited: 22 Jan 2020 17:31
Last modified: 11 Dec 2020 05:01

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