Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures

TL;DR

This paper introduces a deep neural network model that rapidly predicts the near-field and far-field responses of 3D nanostructures, significantly accelerating nanophotonic simulations and design processes.

Contribution

The authors develop a generalized neural network predictor capable of accurately estimating electromagnetic responses of arbitrary nanostructures, outperforming traditional simulation methods in speed.

Findings

Neural network predicts internal fields of nanostructures many orders faster than conventional methods.

Derived physical quantities from predictions reproduce various physical effects accurately.

Approach enables fast, universal design and analysis of nanophotonic systems.

Abstract

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems.