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Record W2907693519 · doi:10.1002/qute.201900021

On‐Chip Integration of Single Photon Sources via Evanescent Coupling of Tapered Nanowires to SiN Waveguides

2019· article· en· W2907693519 on OpenAlexaff

Bibliographic record

VenueAdvanced Quantum Technologies · 2019
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsQuantum dotNanowireCoupling (piping)PhotonWaveguidePhotonicsRidge

Abstract

fetched live from OpenAlex

Abstract A method to integrate nanowire‐based quantum dot single photon sources on‐chip using evanescent coupling is demonstrated. By deterministically placing an appropriately tapered III‐V nanowire, containing a single quantum dot, on top of a silicon‐based ridge waveguide, the quantum dot emission directed toward the taper can be transferred to the ridge waveguide with calculated efficiencies close to 100%. As the evanescent coupling is bidirectional, the source can be optically pumped in both free‐space and through the ridge waveguide. The latter configuration paves the way toward a self‐contained, all‐fiber, plug‐and‐play solution for applications requiring a bright on‐demand single photon source. Using InAsP quantum dots embedded in InP nanowire waveguides, coupling efficiencies to a SiN ridge waveguide of 74% with a single photon purity of 97% are demonstrated. The technique to demonstrate deterministic placement of single quantum emitters onto pre‐fabricated waveguides is used, an important step toward the fabrication of complex quantum photonic circuits.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations57
Published2019
Admission routes1
Has abstractyes

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