Deep Learning and Inverse Design in Plasmonic

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

External Research Organisations

  • University of Ottawa
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Details

Original languageEnglish
Title of host publication19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019
EditorsKarin Hinzer, Joachim Piprek
PublisherIEEE Computer Society
Pages3-4
Number of pages2
ISBN (electronic)9781728116471
Publication statusPublished - 2019
Externally publishedYes
Event19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019 - Ottawa, Canada
Duration: 8 Jul 201912 Jul 2019

Publication series

NameProceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
Volume2019-July
ISSN (Print)2158-3234

Abstract

Laser pulses can colour noble metals by inducing nanoparticles on their surface. The colours are linked to laser parameters and nanoparticles geometry. We apply deep learning to the direct prediction of colours from a laser parameter set or a nanoparticle particle distribution. A new method for inverse design via deep learning is also proposed to retrieve the appropriate laser parameters or nanoparticle distribution given the desired colour.

Keywords

    Deep Learning, FDTD, Inverse Design, Plasmonic Colours

ASJC Scopus subject areas

Cite this

Deep Learning and Inverse Design in Plasmonic. / Baxter, Joshua; Lesina, Antonino Cala; Guay, Jean Michel et al.
19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019. ed. / Karin Hinzer; Joachim Piprek. IEEE Computer Society, 2019. p. 3-4 8806817 (Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD; Vol. 2019-July).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Baxter, J, Lesina, AC, Guay, JM, Weck, A, Berini, P & Ramunno, L 2019, Deep Learning and Inverse Design in Plasmonic. in K Hinzer & J Piprek (eds), 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019., 8806817, Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD, vol. 2019-July, IEEE Computer Society, pp. 3-4, 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019, Ottawa, Canada, 8 Jul 2019. https://doi.org/10.1109/nusod.2019.8806817
Baxter, J., Lesina, A. C., Guay, J. M., Weck, A., Berini, P., & Ramunno, L. (2019). Deep Learning and Inverse Design in Plasmonic. In K. Hinzer, & J. Piprek (Eds.), 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019 (pp. 3-4). Article 8806817 (Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/nusod.2019.8806817
Baxter J, Lesina AC, Guay JM, Weck A, Berini P, Ramunno L. Deep Learning and Inverse Design in Plasmonic. In Hinzer K, Piprek J, editors, 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019. IEEE Computer Society. 2019. p. 3-4. 8806817. (Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD). doi: 10.1109/nusod.2019.8806817
Baxter, Joshua ; Lesina, Antonino Cala ; Guay, Jean Michel et al. / Deep Learning and Inverse Design in Plasmonic. 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019. editor / Karin Hinzer ; Joachim Piprek. IEEE Computer Society, 2019. pp. 3-4 (Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD).
Download
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title = "Deep Learning and Inverse Design in Plasmonic",
abstract = "Laser pulses can colour noble metals by inducing nanoparticles on their surface. The colours are linked to laser parameters and nanoparticles geometry. We apply deep learning to the direct prediction of colours from a laser parameter set or a nanoparticle particle distribution. A new method for inverse design via deep learning is also proposed to retrieve the appropriate laser parameters or nanoparticle distribution given the desired colour.",
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note = "Funding information: ACKNOWLEDGEMTS This work was financially supported by the National Science and Engineering Research Council of Canada, the Canada Research Chairs program, SOSCIP and IBM Canada. We acknowledge the technical support of Scinet.; 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019 ; Conference date: 08-07-2019 Through 12-07-2019",
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