Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “quantum detector”
High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx
<p>This page contains selected data from the peer-reviewed paper "High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx" published in Sensors by Ozhan Koybasi.</p> <p>Description of attached files:</p> <p>Figure 5. Simulated p-polarization reflectance as a function of wavelength for PQEDs mounted in trap configuration with an angle of 15° between the diodes. In this configuration the light beam undergoes 7 reflections: one at 0° degree and two reflections at 15°, 30°, and 45°. The reflectance is reported for six different thicknesses of the SiNx.</p> <p>Figure 6. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness for PQEDs mounted in trap configuration with an angle of 15° between the diodes.</p> <p>Figure 7. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness, when a buffer layer of 6 nm SiO2 is depos-ited before SiNx, for PQEDs mounted in trap configuration with an angle of 15° between the di-odes.</p> <p>Figure 8. Effective lifetime τeff vs. excess carrier density (Δn) for samples prepared with passivation processes described in Table 1.</p> <p>Figure 9. Photoluminescence (PL) lifetime images of samples prepared with the passivation processes described in Table 1.</p> <p>Figure 10. Capacitance—voltage (C—V) measurement results of MIS capacitors prepared with the passivation processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1, at a frequency of 1 kHz.</p> <p>Figure 11. Injection dependent effective minority carrier lifetime τeff (Δn) of test samples passivated with processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1 with simu-lation fits to extract SRV and τbulk.</p> <p>Figure 12. Simulated IQD as a function of reverse bias voltage for p-type inversion-layer photodi-ode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a wavelength of 488 nm.</p> <p>Figure 13. Simulated IQD as a function of wavelength for p-type inversion-layer photodiode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a reverse bias voltage of 5 V.</p> <p>Figure 16. Spatial uniformity of optical power responsivity of the PQEDs with SiO2/SiNx stack photodiodes P18-55-45 (a) and P18-54-44 (b).</p> <p> </p>
Data Part 2: Megascale tomography of a quantum detector
<p>This contains the outcome matrix and the input state matrix, which are used to reconstruct the positive operator valued measures (POVMs) of the detector. The three POVM matrices based on reconstructions with no smoothing, standard next-neighbor smoothing and long-range smoothing as well as the POVMs based on the analytical model are included as well.</p> <p>Wigner function data based on the long-range smoothing and the analytical model POVMs are also included.</p> <p>The raw measurement data that is the basis of this processed data can be found here: <a href="https://doi.org/10.5281/zenodo.10803758" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10803758</a></p>
Data Part 1: Megascale tomography of a quantum detector
<p>This contains the raw time tag data which is used for the megascale tomography. </p> <p>The processed data can be found here: <a href="https://doi.org/10.5281/zenodo.10810148" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10810148</a></p>
Determination of the responsivity of a predictable quantum efficient detector over a wide spectral range based on a 3D model of charge carrier recombination losses
<p>We present a method to determine the internal quantum deficiency (IQD) of a predictable quantum efficient detector (PQED) based on measured photocurrent dependence on bias voltage and a 3D simulation model of charge carrier recombination losses. The simulation model of silicon photodiodes includes wafer doping concentration, fixed charge of SiO2 layer, bulk lifetime of charge carriers and surface recombination velocity as the fitted parameters. With only one set of physical photodiode defining parameters, the simulation shows excellent agreement with experimental data at power levels from 100 μW to 1000 μW with variation in illumination beam size. We could also predict the dependence of IQD on bias voltage at the wavelength of 476 nm using photodiode parameters determined independently at 647 nm wavelength. The fitted values of doping concentration and fixed charge extracted from the simulation model are in close agreement with the expected parameter values determined earlier. At bias voltages larger than 5 V at the wavelength of 476 nm, the internal quantum efficiency of one of the tested PQEDs is measured to be 0.999 970 ± 0.000 027, where the relative expanded uncertainty of 0.000 027 is one of the lowest values ever achieved in spectral responsivity measurement of optical detectors.</p>
A Compact Immersion Grating Spectrometer with Quantum Capacitance Detectors for Space-Borne Far-IR Spectroscopy Project
<p>SPECTROMETER:&nbsp; The grating spectrometer uses a curved grating fabricated on a high resistivity silicon wafer. Light will be coupled in via a silicon immersion lens and coupling waveguide. The wafer profile, including the grating facets are defined by photolithography and deep reactive etching (DRIE) and the wafer is metallized on both sides for better light confinement (except in the regions below where the detector antennas will be placed).&nbsp; The grating both focuses and diffracts the light in the plane of the wafer to a focal arc, where the detectors are positioned. QCD DETECTOR ARRAY: The Quantum Capacitance Detector is a mesoscopic superconducting device that exploits its extreme susceptibility to the presence of quasiparticle excitations arising from pair-breaking radiation to enable background limited detection.&nbsp; QCDs have demonstrated photon noise limited performance&nbsp; at 1.5THz for optical loadings 10-20 to 10-17W.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The detector die will be placed directly on top of the spectrometer wafer and will have an array of QCDs with antennas positioned at the focusing spots of each wavelength.&nbsp; Each detector will be coupled to a superconducting resonator with a different resonant frequency to allow for frequency multiplexing with a single feedline coupling the RF signal to all resonators.&nbsp; A&nbsp; prototype with 32 spectral channels will be demonstrated</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.