1,721,092 research outputs found
Dataset for Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures
No full textDataset supports: Wiecha, P. R. & Muskens, O. L. "Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures". Nano Letters (2019)
Simulation data and analysis</span
Deep learning meets nanophotonics: A generalized Accurate predictor for near fields and far fields of arbitrary 3D nanostructures
No full textDeep 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
Dataset for Quantum Theory of Near-field Optical Imaging with Rare-earth Atomic Clusters
No full textDataset supports the paper: Clément Majorel, Christian Girard, Aurélien Cuche, Arnaud Arbouet, and Peter R. Wiecha "Quantum Theory of Near-field Optical Imaging with Rare-earth Atomic Clusters". JOSA B 37(5), 1474-1484 (2020)</span
Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array
No full textWe demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral transmission matrix. Optimized neural networks trained on these datasets achieve reliable reconstruction of both discrete and continuous spectra from a monochromatic camera image. Deep learning is compared to analytical inversion methods as well as to a compressive sensing algorithm and shows favourable characteristics both in the oversampling and in the sparse undersampling (compressive) regimes. The deep learning approach offers significant advantages in robustness to drift or noise and in reconstruction speed. In a proof-of-principle demonstrator we achieve real time recovery of hyperspectral information using a multi-core, multi-mode fiber array as a random scattering medium.</p
Dataset for Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array
No full textThis dataset supports the paper
Kürüm, U., Wiecha, P.R., French, R., & Muskens, O. L. (2018). Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array. Optics Express, DOI:10.1364/OE.27.020965</span
Dataset for Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size
No full textRaw data for numerical simulation results that support the paper
"Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size" published in ACS Photonics.
"dataset.zip" containing folders "figX" with X from 1 to 8
Each figure's dataset is bundled in an according subfolder. The data is labelled in an understandable form, if applicable headers have been added to raw-data textfiles.</span
Dataset for Polarizabilities of complex individual dielectric or plasmonic nanostructures
No full textDataset for supports the paper: Adelin Patoux, Clément Majorel, Peter R. Wiecha, Aurélien Cuche, Otto L. Muskens, Christian Girard, and Arnaud Arbouet "Polarizabilities of complex individual dielectric or plasmonic nanostructures". Physical Review B (2020)
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Dataset for Design of Plasmonic Directional Antennas via Evolutionary Optimization
No full textRaw data supporting the paper
P.R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O.L. Muskens & A. Arbouet. (2019). Design of plasmonic directional antennas via evolutionary optimization. Optics Express</span
Polarization conversion in plasmonic nanoantennas for metasurfaces using structural asymmetry and mode hybridization
No full textData in support of the paper by Peter R. Wiecha, Leo-Jay Black, Yudong Wang, Vincent Paillard, Christian Girard, Otto L. Muskens & Arnaud Arbouet entitled "Polarization conversion in plasmonic nanoantennas for metasurfaces using structural asymmetry and mode hybridization" in Scientific Reports 7, Article number: 40906 (2017) doi:10.1038/srep40906</span
Inverse design of unitary transmission matrices in silicon photonic coupled waveguide arrays using a neural adjoint model
No full textThe development of low-loss reconfigurable integrated optical devices enables further research into technologies including photonic signal processing, analogue quantum computing, and optical neural networks. Here, we introduce digital patterning of coupled waveguide arrays as a platform capable of implementing unitary matrix operations. Determining the required device geometry for a specific optical output is computationally challenging and requires a robust and versatile inverse design protocol. In this work we present an approach using high speed neural network surrogate-based gradient optimization, capable of predicting patterns of refractive index perturbations based on switching of the ultralow loss chalcogenide phase change material, antimony triselinide (Sb
2Se
3). Results for a 3 × 3 silicon waveguide array are presented, demonstrating control of both amplitude and phase for each transmission matrix element. Network performance is studied using neural network optimization tools such as data set augmentation and supplementation with random noise, resulting in an average fidelity of 0.94 for unitary matrix targets. Our results show that coupled waveguide arrays with perturbation patterns offer new routes for achieving programmable unitary operators, or Hamiltonians for quantum simulators, with a reduced footprint compared to conventional interferometer-mesh technology.
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