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20 results for “Silicon photonics”
Data and code for article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides"
<p>This dataset contains the data presented in the figures of the article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides" (doi:10.1038/s41566-018-0144-1).</p> <p>The raw data in figures (curved plots) is packaged as an independent OriginLab project file (.opj). </p> <p>The layout of the design of the silicon nitride nano-photonic waveguide is presented. Fabrication process card (shown as a diagram) is provided as well.</p> <p>The source code for simulations presented in the article is also presented.</p>
Data for "Two-stage, low noise quantum frequency conversion of single photons from silicon-vacancy centers in diamond to the telecom C-band"
<p>The silicon-vacancy center in diamond holds great promise as a qubit for quantum communication networks. However, since the optical transitions are located within the visible red spectral region, quantum frequency conversion to low-loss telecommunication wavelengths becomes a necessity for its use in long-range, fiber-linked networks. This work presents a highly efficient, low-noise quantum frequency conversion device for photons emitted by a silicon-vacancy (SiV) center in diamond to the telecom C-band. By using a two-stage difference-frequency mixing scheme SPDC noise is circumvented and Raman noise is minimized, resulting in a very low noise rate of 10.4(7) photons per second as well as an overall device efficiency of 35.6 %. By converting single photons from SiV centers we demonstrate the preservation of photon statistics upon conversion.</p>
Data from: Hybrid Integration of Silicon Photonic Devices on Lithium Niobate for Optomechanical Wavelength Conversion
<p>Source data for Figures.</p>
Replication Data for "Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication"
<p>COMSOL files and Python post-processing code.</p>
Data: Multilayer integration in silicon nitride: decoupling linear and nonlinear functionalities for ultralow loss photonic integrated systems
<p>This folder contains the data relative to the paper with title:<br> Multilayer integration in silicon nitride: decoupling linear and nonlinear functionalities for ultralow loss photonic integrated systems<br> by<br> Marcello Girardi, Óskar Helgason, Alexander Caut, Magnus Karlsson, Anders Larsson, Victor Torres Company</p> <p>Chalmers University of Technology</p>
Indistinguishable photons from an artificial atom in silicon photonics [Data]
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Generation of Entangled Photon Pairs from a Silicon Bichromatic Photonic Crystal Cavity
<p>Integrated quantum photonics leverages the on-chip generation of nonclassical states of light to realize key function-<br>alities of quantum devices. Typically, the generation of such nonclassical states relies on whispering gallery mode<br>resonators, such as integrated optical micro-rings, which enhance the efficiency of the underlying spontaneous non-<br>linear processes. While this kind of resonators excel in maximizing either the temporal confinement or the spatial<br>overlap between different resonant modes, they are usually associated with large mode volumes, imposing an intrin-<br>sic limitation on the device efficiency and footprint. Here, we engineer a source of time-energy entangled photon<br>pairs based on a silicon photonic crystal cavity, implemented in a fully CMOS-compatible platform. In this device,<br>resonantly enhanced spontaneous four-wave mixing converts pump photon pairs into signal/idler photon pairs at the<br>energy-conserving condition in the telecommunication C-band. The design of the resonator is based on an effective<br>bichromatic confinement potential, allowing to achieve up to 9 close-to-equally spaced modes in frequency, while pre-<br>serving small mode volumes, and the whole chip, including grating couplers and access waveguides, is fabricated in<br>a single run on a silicon-on-insulator platform. Besides demonstrating efficient photon pair generation, we also im-<br>plement a Franson-type interference experiment, demonstrating entanglement between signal and idler photons with<br>a Bell inequality violation exceeding 5 standard deviations. The high generation efficiency combined with the small<br>device footprint in a CMOS-compatible integrated structure opens a pathway towards the implementation of compact<br>quantum light sources in all-silicon photonic platforms.</p>
Data for Deep Reactive Ion Etching of Cylindrical Nanopores in Silicon for Photonic Crystals
<p>Data for Deep Reactive Ion Etching of Cylindrical Nanopores in Silicon for Photonic Crystals</p>
Dataset for "Strong coupling between a photon and a hole spin in silicon"
<p>This dataset contains the raw data and its analysis code to generate the figures of the publication entitled : "Strong coupling between a photon and a hole spin in silicon". Please have a look at the readme.txt file for further information.</p>
A large-scale microelectromechanical-systems-based silicon photonics LiDAR
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Data for manuscript "Integrated microwave photonic notch filter using a heterogeneously integrated Brillouin and active-silicon photonic circuit"
<p>Experimental data and scripts to plot results for manuscript "Integrated microwave photonic notch filter using a heterogeneously integrated Brillouin and active-silicon photonic circuit"</p>
Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation
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Data for manuscript "Individually Addressable and Spectrally Programmable Artificial Atoms in Silicon Photonics"
<p>Data in scientific manuscript published in preprint: https://arxiv.org/abs/2202.02342</p>
Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation - Supporting Information
<p>Photonic integrated circuits (PICs) are revolutionizing the realm of information technology, promising unprecedented speeds and efficiency in data processing and optical communication. However, the nanoscale precision required to fabricate these circuits at scale presents significant challenges, due to the need to maintain consistency across wavelength-selective components, which necessitates individualized adjustments after fabrication. Harnessing spectral alignment by automated silicon ion implantation, in this work scalable and non-volatile photonic computational memories are demonstrated in high-quality resonant devices. Precise spectral trimming of large-scale photonic ensembles from a few picometers to several nanometres is achieved with long-term stability and marginal loss penalty. Based on this approach, spectrally aligned photonic memory and computing systems for general matrix multiplication are demonstrated, enabling wavelength multiplexed integrated architectures at large scales.</p>
High-Quality Amorphous Silicon Carbide for Hybrid Photonic Integration Deposited at a Low Temperature - Supportive Information
<p>Integrated photonic platforms have proliferated in recent years, each demonstrating its own unique strengths and shortcomings. However, given the processing incompatibilities of different platforms, a formidable challenge in the field of integrated photonics still remains for combining the strength of different optical materials in one hybrid integrated platform. Silicon carbide is a material of great interest because of its high refractive index, strong second and third-order non-linearities and broad transparecy window in the visible and near infrared. However, integrating SiC has been difficult, and current approaches rely on transfer bonding techniques, that are time consuming, expensive and lacking precision in layer thickness. Here, we demonstrate high index Amorphous Silicon Carbide (a-SiC) films deposited at 150∘C and verify the high performance of the platform by fabricating standard photonic waveguides and ring resonators. </p>
Converting microwave and telecom photons with a silicon photonic nanomechanical interface
<p>This datasets comprises all data shown in plots of the submitted article "Converting microwave and telecom photons with a silicon photonic nanomechanical interface". Additional raw data are available from the corresponding author on reasonable request.</p>
High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits
<p>Available data for "High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits"</p>
Near ultraviolet photonic integrated lasers based on silicon nitride
<p>This dataset contains the data presented in the figures of the paper "Near ultraviolet photonic integrated lasers based on silicon nitride".</p>
Open data for "High-performance lasers for fully integrated silicon nitride photonics"
<p>Available data for the results presented in "High-performance lasers for fully integrated silicon nitride photonics".</p>
Clinical Feasibility and Evaluation of Silicon Photon Counting CT
ClinicalTrials.gov study NCT05838482. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.