pyGDM—A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures

Wiecha, Peter R.

doi:10.1016/j.cpc.2018.06.017

Cited by 58 publications

(60 citation statements)

References 73 publications

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“…By including a surface propagator we can take into account a glass substrate [44]. Details on our EO-GDM technique can be found elsewhere [33,42]. We want to note, that any numerical approach for solving Maxwell's equations can be used together with EO (possible other methods are for example finite difference time domain [28] or mode expansion techniques [32]).…”

Section: Optimization Method Model and Problemmentioning

confidence: 99%

Design of plasmonic directional antennas via evolutionary optimization

Wiecha¹,

Majorel²,

Girard³

et al. 2019

Opt. Express

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We demonstrate inverse design of plasmonic nanoantennas for directional light scattering. Our method is based on a combination of full-field electrodynamical simulations via the Green dyadic method and evolutionary optimization (EO). Without any initial bias, we find that the geometries reproducibly found by EO, work on the same principles as radio-frequency antennas. We demonstrate the versatility of our approach by designing various directional optical antennas for different scattering problems. EO based nanoantenna design has tremendous potential for a multitude of applications like nano-scale information routing and processing or single-molecule spectroscopy. Furthermore, EO can help to derive general design rules and to identify inherent physical limitations for photonic nanoparticles and metasurfaces.

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Section: Optimization Method Model and Problemmentioning

confidence: 99%

Design of plasmonic directional antennas via evolutionary optimization

Wiecha¹,

Majorel²,

Girard³

et al. 2019

Opt. Express

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“…Using according Green's dyads, the optical electric and magnetic fields (and in consequence the Poynting vector) can be obtained at any location outside the nanostructure (see Figure 1(c-d)). 45 Extinction, scattering or absorption cross sections can be calculated almost effortless, 1 as well as the polarization state and spatial patterns of the scattering (Figure 1e), 5,46 the dissipated heat or local temperature gradients, 7,47 nonlinear effects like second or third harmonic generation and multi-photon luminescence 10,48,49 or the multipole decomposition of the optical response. 2 In consequence, an ANN capable to accurately predict the internal electric fields of a photonic nanostructure represents a generalized, phenomenological model of light-matter interaction, with tremendous potentials for rapid nano-optical simulations.…”

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confidence: 99%

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

2019

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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.

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“…46 In particular, we use an own implementation in python, "pyGDM". 47 In the GDM the volume of a nanostructure is discretized with N cubic meshpoints of edge length d.…”

Section: Green Dyadic Methodsmentioning

confidence: 99%

Pushing the limits of optical information storage using deep learning

et al. 2019

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Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust read-out schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning based approach in which the scattering spectra are analyzed by an artificial neural network, we achieve quasi error free read-out of sequences of up to 9 bit, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production ready CMOS technology.Optical information storage promises perennial longevity, high information densities and low energy consumption compared to magnetic storage media. 1,2The compact disc (CD) and its successors, the DVD and the blue-ray disc, broadly established optical storage in our society. 3,4 Those media are based on storing a single binary digit per diffraction limited area ("zero" or "one"). Several concepts have been proposed to increase the information density in optical storage. Examples are schemes exploiting polarization-sensitive digits, 5 near-field optical recording, 6 the use of fluorescent dyes 7 or three-dimensional approaches like two-photon point-excitation 8 . Yet, all these alternatives suffer from major drawbacks. Either they are hardly superior to commercial planar solutions (polarization-sensitive patterns) or they require very complex storage media (fluorescence) or sophisticated read-out schemes (nearfield recording, two-photon point-excitation). The most promising alternative seemed to be holographic memory, which was proposed in the early 1960ies and makes use of the volume of the storage medium. To date, however, there is still no commercial product available, despite several announcements in the past 20 years. 9,10 In the last decades, photonic nanostructures emerged as powerful instruments to control light at the nanometer scale. 11,12 Localized surface plasmons (LSP) in metal nanoparticles 13 or Mie-type resonances in high-index dielectric structures 14 cover the entire visible spectrum and can be tuned by designing appropriate geometric features. 15,16 Furthermore, the high scattering efficiencies of photonic nanostructures render single-particle spectroscopy relatively easy. In consequence, the idea has been raised to encode information in the rich scattering spectra of plasmonic nanostructures, denser than a single data bit. 17-21 The information density might be even further increased by addre...

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pyGDM—A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures

Cited by 58 publications

References 73 publications

Design of plasmonic directional antennas via evolutionary optimization

Design of plasmonic directional antennas via evolutionary optimization

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

Pushing the limits of optical information storage using deep learning

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