Deep Learning and Inverse Design in Plasmonic

authored by

Joshua Baxter, Antonino Cala Lesina, Jean Michel Guay, Arnaud Weck, Pierre Berini, Lora Ramunno

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.

External Organisation(s)

University of Ottawa

Type

Conference contribution

Pages

3-4

No. of pages

2

Publication date

2019

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Electrical and Electronic Engineering, Modelling and Simulation

Electronic version(s)

https://doi.org/10.1109/nusod.2019.8806817 (Access: Closed)