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Record W3015198129 · doi:10.1021/acs.nanolett.0c00607

Multiplexed Single-Photon Source Based on Multiple Quantum Dots Embedded within a Single Nanowire

2020· article· en· W3015198129 on OpenAlexafffund
Patrick Laferrière, Edith Yeung, Lambert Giner, S. Haffouz, J. Lapointe, G. C. Aers, Philip J. Poole, Robin L. Williams, Dan Dalacu

Bibliographic record

VenueNano Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsQuantum dotNanowireSingle-photon sourcePhotonPhotonicsOptoelectronicsSpontaneous emissionMultiplexingPhysicsMaterials scienceOpticsLaserTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Photonics-based quantum information technologies require efficient, high emission rate sources of single photons. Position-controlled quantum dots embedded within a broadband nanowire waveguide provide a fully scalable route to fabricating highly efficient single-photon sources. However, emission rates for single-photon devices are limited by radiative recombination lifetimes. Here, we demonstrate a multiplexed single-photon source based on a multidot nanowire. Using epitaxially grown nanowires, we incorporate multiple energy-tuned dots, each optimally positioned within the nanowire waveguide, providing single photons with high efficiency. This linear scaling of the single-photon emission rate with number of emitters is demonstrated using a five-dot nanowire with an average multiphoton emission probability of <4% when excited at saturation. This represents the first ever demonstration of multiple single-photon emitters deterministically incorporated in a single photonic device and is a major step toward achieving GHz single-photon emission rates from a scalable multi-quantum-dot system.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.197
Teacher spread0.178 · 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

Citations42
Published2020
Admission routes2
Has abstractyes

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