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Record W4225472609 · doi:10.1038/s41598-022-10451-1

Unity yield of deterministically positioned quantum dot single photon sources

2022· article· en· W4225472609 on OpenAlexafffund
Patrick Laferrière, Edith Yeung, Isabelle Miron, David B. Northeast, S. Haffouz, J. Lapointe, Marek Korkusiński, Philip J. Poole, Robin L. Williams, Dan Dalacu

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsYield (engineering)Quantum dotPhotonQuantum yieldPhysicsComputer scienceOptoelectronicsOptics

Abstract

fetched live from OpenAlex

We report on a platform for the production of single photon devices with a fabrication yield of 100%. The sources are based on InAsP quantum dots embedded within position-controlled bottom-up InP nanowires. Using optimized growth conditions, we produce large arrays of structures having highly uniform geometries. Collection efficiencies are as high as 83% and multiphoton emission probabilities as low as 0.6% with the distribution away from optimal values associated with the excitation of other charge complexes and re-excitation processes, respectively, inherent to the above-band excitation employed. Importantly, emission peak lineshapes have Lorentzian profiles indicating that linewidths are not limited by inhomogeneous broadening but rather pure dephasing, likely elastic carrier-phonon scattering due to a high phonon occupation. This work establishes nanowire-based devices as a viable route for the scalable fabrication of efficient single photon sources and provides a valuable resource for hybrid on-chip platforms currently being developed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2022
Admission routes2
Has abstractyes

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