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Record W4318245377 · doi:10.1021/acs.nanolett.2c04375

Position-Controlled Telecom Single Photon Emitters Operating at Elevated Temperatures

2023· article· en· W4318245377 on OpenAlexafffund
Patrick Laferrière, S. Haffouz, David B. Northeast, Philip J. Poole, Robin L. Williams, Dan Dalacu

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsPhotonPhotonicsSingle-photon sourceOptoelectronicsWavelengthQuantum key distributionQuantum dotSingle-mode optical fiberMaterials scienceOptical communicationExcitationPhysicsOpticsPurcell effectSpontaneous emissionOptical fiberLaser

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide A key resource in quantum-secured communication protocols are single photon emitters. For long-haul optical networks, it is imperative to use photons at wavelengths compatible with telecom single mode fibers. We demonstrate high purity single photon emission at 1.31 μm using deterministically positioned InP photonic waveguide nanowires containing single InAsP quantum dot-in-a-rod structures. At excitation rates that saturate the emission, we obtain a single photon collection efficiency at first lens of 27.6% and a probability of multiphoton emission of g (2) (0) = 0.021. We have also evaluated the performance of the source as a function of temperature. Multiphoton emission probability increases with temperature with values of 0.11, 0.34, and 0.57 at 77, 220 and 300 K, respectively, which is attributed to an overlap of temperature-broadened excitonic emission lines. These results are a promising step toward scalably fabricating telecom single photon emitters that operate under relaxed cooling requirements.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.085
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2023
Admission routes2
Has abstractyes

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