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Record W2047387949 · doi:10.1116/1.1487876

Monolithic multiple wavelength ridge waveguide laser array fabricated by Nd:YAG laser-induced quantum well intermixing

2002· article· en· W2047387949 on OpenAlexaff
Jan J. Dubowski, Yan Feng, Philip J. Poole, M. Buchanan, S. Poirier, Jonathan Genest, Vincent Aimez

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsUniversité de SherbrookeNational Research Council Canada
Fundersnot available
KeywordsLaserMaterials scienceOptoelectronicsFabricationPhotonicsPhotonic integrated circuitWavelengthQuantum wellOpticsWaveguidePhysics

Abstract

fetched live from OpenAlex

Maskless selective area cw Nd:YAG laser annealing of GaInAsP/InP quantum well (QW) structures has been investigated as a possible route towards the fabrication of monolithically integrated photonic circuits. Laser irradiation of a 5 QW laser structure, originally designed for lasers emitting at 1.52 μm, yielded material having a continuously changing band gap ranging from 1.52 to 1.4 μm over the distance of about 3 mm. Bars with arrays of ridge waveguide lasers, having cavity lengths from 300 to 600 μm, were fabricated from the processed material. An individual bar, 2–3 mm long, comprised lasers operating typically between 1.4 and 1.52 μm. The lasers showed stable threshold current density and high quantum efficiency at all operating wavelengths. The results indicate that the applied approach has the potential to realize the cost-effective fabrication of advanced photonic devices and photonic integrated 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 science0.0010.000
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 head0.014
GPT teacher head0.219
Teacher spread0.204 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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