Simultaneous inverse design of materials and parameters of core-shell nanoparticle via deep-learning: Demonstration of dipole resonance engineering

TL;DR

This paper demonstrates a deep-learning approach for the simultaneous inverse design of core-shell nanoparticles, enabling spectral tuning and resonance engineering for advanced nanophotonic applications.

Contribution

It introduces a neural network model that jointly optimizes material and structural parameters for desired optical resonances in core-shell nanoparticles.

Findings

Successfully designed nanoparticles with targeted electric and magnetic dipole resonances.

Achieved spectral tuning of electric dipole resonances.

Identified configurations with spectrally isolated and overlapped resonances.

Abstract

Recent introduction of data-driven approaches based on deep-learning technology has revolutionized the field of nanophotonics by allowing efficient inverse design methods. In this paper, simultaneous inverse design of materials and structure parameters of core-shell nanoparticle is achieved for the first time using deep-learning of a neural network. A neural network to learn correlation between extinction spectra of electric and magnetic dipoles and core-shell nanoparticle designs, which include material information and shell thicknesses, is developed and trained. We demonstrate deep-learning-assisted inverse design of core-shell nanoparticle for 1) spectral tuning electric dipole resonances, 2) finding spectrally isolated pure magnetic dipole resonances, and 3) finding spectrally overlapped electric dipole and magnetic dipole resonances. Our finding paves the way of the rapid development of nanophotonics by allowing a practical utilization of a deep-learning technology for nanophotonic inverse design.