Publications of the Research Group Computational Photonics

Prof. Dr. Antonio Calà Lesina

Plasmonic colours predicted by deep learning

authored by
Joshua Baxter, Antonino Calà Lesina, Jean Michel Guay, Arnaud Weck, Pierre Berini, Lora Ramunno
Abstract

Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition processes. This technology provides two datasets which we use to train artificial neural networks, data from the experiment itself (laser parameters vs. colours) and data from the corresponding numerical simulations (geometric parameters vs. colours). We apply deep learning to predict the colour in both cases. We also propose a method for the solution of the inverse problem – wherein the geometric parameters and the laser parameters are predicted from colour – using an iterative multivariable inverse design method.

External Organisation(s)
University of Ottawa
Type
Article
Journal
Scientific reports
Volume
9
ISSN
2045-2322
Publication date
30.05.2019
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
General
Electronic version(s)
https://doi.org/10.1038/s41598-019-44522-7 (Access: Open)