Abstract:Abstract. A novel method is elaborated for the electromagnetic scattering from periodical arrays of scatterers embedded in a polarizable background. A dyadic periodic Green's function is introduced to calculate the scattered electric field in a lattice of dielectric or metallic objects. The method exhibits strong advantages: discretization and computation of the field are restricted to the volume of the scatterers in the unit cell, open and periodic boundary conditions for the electric field are included in th… Show more
“…Periodic structures can be treated using training data describing one unit-cell of the periodic structure. 69,70 In addition, the approach can be extended to spectrally resolved predictions using multiple output layers. Spectral training might be accelerated by transfer learning.…”
Section: Nano Lettersmentioning
confidence: 99%
“…However, the method could be further generalized to arbitrary hybrid-material structures using the three-dimensional distribution of the dielectric constant as input. Periodic structures can be treated using training data describing one unit-cell of the periodic structure. , In addition, the approach can be extended to spectrally resolved predictions using multiple output layers. Spectral training might be accelerated by transfer learning.…”
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.
“…Periodic structures can be treated using training data describing one unit-cell of the periodic structure. 69,70 In addition, the approach can be extended to spectrally resolved predictions using multiple output layers. Spectral training might be accelerated by transfer learning.…”
Section: Nano Lettersmentioning
confidence: 99%
“…However, the method could be further generalized to arbitrary hybrid-material structures using the three-dimensional distribution of the dielectric constant as input. Periodic structures can be treated using training data describing one unit-cell of the periodic structure. , In addition, the approach can be extended to spectrally resolved predictions using multiple output layers. Spectral training might be accelerated by transfer learning.…”
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.
“…• periodic structures [75,76] • quantum corrected model for plasmonic tunneling currents via junctions of inhomogeneous permittivity [77] • SNOM image calculation/interpretation [78,79] • memory-efficient conjugate gradients solver including FFT-accelerated matrix-vector multiplications for large problems [27] 13. Appendix -Keyword arguments of the most important classes and functions…”
pyGDM is a python toolkit for electro-dynamical simulations in nano-optics based on the Green Dyadic Method (GDM). In contrast to most other coupled-dipole codes, pyGDM uses a generalized propagator, which allows to cost-efficiently solve large monochromatic problems such as polarization-resolved calculations or raster-scan simulations with a focused beam or a quantum-emitter probe. A further peculiarity of this software is the possibility to very easily solve 3D problems including a dielectric or metallic substrate. Furthermore, pyGDM includes tools to easily derive several physical quantities such as far-field patterns, extinction and scattering cross-section, the electric and magnetic near-field in the vicinity of the structure, the decay rate of quantum emitters and the LDOS or the heat deposited inside a nanoparticle. Finally, pyGDM provides a toolkit for efficient evolutionary optimization of nanoparticle geometries in order to maximize (or minimize) optical properties such as a scattering at selected resonance wavelengths.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.