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Record W2772593386 · doi:10.1109/ipcon.2017.8116092

Bright single InAs quantum dots at telecom wavelengths in site-selective InP nanowires

2017· article· en· W2772593386 on OpenAlexaff
S. Haffouz, Dan Dalacu, Philip J. Poole, Khaled Mnaymneh, J. Lapointe, G. C. Aers, D. Poltras, Robin L. Williams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSingle-photon sourceQuantum dotPhotonNanowireOptoelectronicsPhysicsQuantum opticsWavelengthPhotonicsOpticsMaterials scienceTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Non-classical light sources that can produce streams of correlated on-demand photons are a central building block for optics based quantum information technologies. There are numerous possible approaches for producing such a light source. One of the most promising is the solid-state single photon source based on a single quantum dot in III-V semiconductors. Utilizing a single InAs quantum dot in an InP nanowire, we previously demonstrated a bright and efficient source for single photons [1] and entangled photon pairs [2] that emits around 950 nm. In order to interface with telecom systems, single photon sources emitting at longer wavelengths are required. A few works have extended the emission to the telecom band using a single InAs/InP quantum dot in a micro-cavity [3-4]. However, improving the source brightness and the extraction efficiency remains a challenging task. In this contribution, by modifying the growth conditions and the pre-growth pattern for an InAs dot in an InP nanowire, we demonstrate a bright light source that emits in the telecom O-band, an important step towards the demonstration of a single photon source.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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