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Record W2008197705 · doi:10.1116/1.582229

Widely tunable self-assembled quantum dot lasers

2000· article· en· W2008197705 on OpenAlexafffund
Karin Hinzer, Claudine Nì. Allen, J. Lapointe, Damien Picard, Z. R. Wasilewski, Simon Fafard, A. J. SpringThorpe

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNortel (Canada)Institute for Microstructural Sciences
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLasing thresholdQuantum dotWetting layerLaser linewidthOptoelectronicsExcited stateQuantum dot laserMaterials scienceLaserGain-switchingDiodeMolecular beam epitaxyWavelengthSemiconductor laser theoryOpticsLayer (electronics)Atomic physicsEpitaxyPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Quantum dot laser diodes with up to five well-defined electronic shells are fabricated using self-assembled quantum dots (QDs) grown by molecular beam epitaxy. At 77 K, we tune the lasers from the first to the fourth excited state of the QDs by varying the cavity length, this covers a wavelength range from 869 to 963 nm. At room temperature, we obtain lasing from the second to the fourth excited state covering the 938 to 984 nm wavelength range. For high injection currents, a large part of the QD ensemble contributes at once to the stimulated emission yielding a lasing emission linewidth having a full width at half maximum of 25 nm. By increasing the energy spacing between the QD energy level contributing to the lasing and the wetting layer energy levels, improved thresholds at higher temperatures are observed, leading to lasing below 100 A/cm2 at room temperature.

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 score0.003

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations14
Published2000
Admission routes2
Has abstractyes

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