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

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.

arXiv comment10 pages, 5 figures + supporting information pdf of 11 pages, 10 figures

References in corpus (16)

• Training deep neural networks for the inverse design of nanophotonic structures
• Phase recovery and holographic image reconstruction using deep learning in neural networks
• A Generative Model for Inverse Design of Metamaterials
• Global optimization of dielectric metasurfaces using a physics-driven neural network
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• DNA nanotechnology-enabled chiral plasmonics: from static to dynamic
• Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers
• Evolutionary optimization of optical antennas
• Evolutionary Multi-Objective Optimisation of Colour Pixels based on Dielectric Nano-Antennas
• Pushing the limits of optical information storage using deep learning
• Strongly directional scattering from dielectric nanowires
• pyGDM -- A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures
• Origin of Second Harmonic Generation from individual Silicon Nanowires
• Designing Thermoplasmonic Properties of Metallic Metasurfaces
• A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks
• A Hybrid Strategy for the Discovery and Design of Photonic Nanostructures

Cited by in corpus (28)

• Deep neural networks for the evaluation and design of photonic devices
• Deep learning in nano-photonics: inverse design and beyond
• Data-Driven Design for Metamaterials and Multiscale Systems: A Review
• Intelligent metaphotonics empowered by machine learning
• Elucidating the Behavior of Nanophotonic Structures Through Explainable Machine Learning Algorithms
• A newcomer's guide to deep learning for inverse design in nano-photonics
• A topological encoding method for data-driven photonics inverse design
• A neural operator-based surrogate solver for free-form electromagnetic inverse design
• Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size
• Fast Design of Plasmonic Metasurfaces Enabled by Deep Learning
• pyGDM -- new functionalities and major improvements to the python toolkit for nano-optics full-field simulations
• Physics-informed reinforcement learning for sample-efficient optimization of freeform nanophotonic devices
• Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials
• Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres
• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
• A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks
• Deep learning for nano-photonic materials -- The solution to everything!?
• Decision-making and control with diffractive optical networks
• Multiscale Physics-Informed Neural Networks for the Inverse Design of Hyperuniform Optical Materials
• Inverse Design of Unitary Transmission Matrices in Silicon Photonic Coupled Waveguide Arrays using a Neural Adjoint Model
• Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks
• Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers
• Identifying topology of leaky photonic lattices with machine learning
• Broadband Polarization-Independent Achromatic Metalenses with Unintuitively-Designed Random-Shaped Meta-Atoms
• On-chip visible light communication-band metasurface modulators with niobium plasmonic nano-antenna arrays
• Deep learning estimation of complex reverberant wave fields by a programmable metasurface
• Generalizing the exact multipole expansion: Density of multipole modes in complex photonic nanostructures
• Data-Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning