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Record W1974859542 · doi:10.1063/1.4864131

High quantum efficiency and low dark count rate in multi-layer superconducting nanowire single-photon detectors

2014· article· en· W1974859542 on OpenAlexafffund
Amir Jafari Salim, Amin Eftekharian, A. Hamed Majedi

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowirePhotonPhysicsVortexCondensed matter physicsDetectorQuantum efficiencySuperconductivityAbsorption (acoustics)OptoelectronicsOptics

Abstract

fetched live from OpenAlex

In this paper, we theoretically show that a multi-layer superconducting nanowire single-photon detector (SNSPD) is capable of approaching characteristics of an ideal SNSPD in terms of the quantum efficiency, dark count, and band-width. A multi-layer structure improves the performance in two ways. First, the potential barrier for thermally activated vortex crossing, which is the major source of dark counts and the reduction of the critical current in SNSPDs is elevated. In a multi-layer SNSPD, a vortex is made of 2D-pancake vortices that form a stack. It will be shown that the stack of pancake vortices effectively experiences a larger potential barrier compared to a vortex in a single-layer SNSPD. This leads to an increase in the experimental critical current as well as significant decrease in the dark count rate. In consequence, an increase in the quantum efficiency for photons of the same energy or an increase in the sensitivity to photons of lower energy is achieved. Second, a multi-layer structure improves the efficiency of single-photon absorption by increasing the effective optical thickness without compromising the single-photon sensitivity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.232
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2014
Admission routes2
Has abstractyes

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