Plasmonic colours predicted by deep learning

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.

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Cited by in corpus (7)

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• Machine learning identifies scale-free properties in disordered materials
• Over 30,000-fold field enhancement of terahertz nanoresonators enabled by rapid inverse design
• Topology optimization of dispersive plasmonic nanostructures in the time-domain