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Record W3086335469 · doi:10.1063/5.0020681

Single quantum dot-in-a-rod embedded in a photonic nanowire waveguide for telecom band emission

2020· article· en· W3086335469 on OpenAlexafffund
S. Haffouz, Philip J. Poole, Jeongwan Jin, Xiaohua Wu, L. Ginet, Khaled Mnaymneh, Dan Dalacu, Robin L. Williams

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersCanadian Space Agency
KeywordsQuantum dotNanowireOptoelectronicsMaterials scienceWaveguideBand gapPhotonic crystalOpticsPhysics

Abstract

fetched live from OpenAlex

Bright emission from non-classical light sources is a key requirement for their practical use in quantum optics. In this Letter, we report on an alternative approach to realize high-brightness nanowire emitters in the telecom band. We discuss the growth and optical properties of a single InAs0.68P0.32 quantum dot in an InAs0.50P0.50 quantum rod, all embedded in an InP nanowire waveguide. Modifying the bandgap energy of the matrix surrounding the quantum dot by inserting it into an InAs0.50P0.50 quantum rod, instead of InP, reduces the barrier height for carriers in the dot. As a result, light emission at λ = 1310 nm is reached from an InAs0.68P0.32 dot grown with the same deposition conditions as that used for λ = 950 nm emission in the conventional structure. We demonstrate that the dot-in-a-rod (DROD) configuration increases (up to fivefold) the emission rate of the emitters at 1310–1550 nm as compared to those grown with the higher dot aspect ratio required when not using the DROD structure. Carrier generation localized to the dot (quasi-resonant scheme) is achieved by optically pumping the rod below the InP bandgap.

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 score0.166
Threshold uncertainty score0.903

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 head0.015
GPT teacher head0.214
Teacher spread0.200 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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