Details
| Original language | English |
|---|---|
| Title of host publication | 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019 |
| Editors | Karin Hinzer, Joachim Piprek |
| Publisher | IEEE Computer Society |
| Pages | 3-4 |
| Number of pages | 2 |
| ISBN (electronic) | 9781728116471 |
| Publication status | Published - 2019 |
| Externally published | Yes |
| Event | 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019 - Ottawa, Canada Duration: 8 Jul 2019 → 12 Jul 2019 |
Publication series
| Name | Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD |
|---|---|
| Volume | 2019-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
- Engineering(all)
- Electrical and Electronic Engineering
- Mathematics(all)
- Modelling and Simulation
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
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 proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep Learning and Inverse Design in Plasmonic
AU - Baxter, Joshua
AU - Lesina, Antonino Cala
AU - Guay, Jean Michel
AU - Weck, Arnaud
AU - Berini, Pierre
AU - Ramunno, Lora
N1 - 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.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Deep Learning
KW - FDTD
KW - Inverse Design
KW - Plasmonic Colours
UR - http://www.scopus.com/inward/record.url?scp=85071875348&partnerID=8YFLogxK
U2 - 10.1109/nusod.2019.8806817
DO - 10.1109/nusod.2019.8806817
M3 - Conference contribution
AN - SCOPUS:85071875348
T3 - Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
SP - 3
EP - 4
BT - 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019
A2 - Hinzer, Karin
A2 - Piprek, Joachim
PB - IEEE Computer Society
T2 - 19th International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2019
Y2 - 8 July 2019 through 12 July 2019
ER -