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Record W2072700646 · doi:10.1063/1.1884745

Effect of template morphology on the efficiency of InGaN∕GaN quantum wells and light-emitting diodes grown by molecular-beam epitaxy

2005· article· en· W2072700646 on OpenAlexaff
H. Tang, S. Haffouz, A. Powell, J. A. Bardwell, J. B. Webb

Bibliographic record

VenueApplied Physics Letters · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsMolecular beam epitaxyOptoelectronicsQuantum wellMaterials scienceIndiumSapphireMetalorganic vapour phase epitaxyChemical vapor depositionIndium nitrideEpitaxyDiodeIndium gallium nitrideLight-emitting diodeSurface roughnessPhotoluminescenceWide-bandgap semiconductorGallium nitrideNanotechnologyOpticsLaserLayer (electronics)Composite material

Abstract

fetched live from OpenAlex

The pronounced enhancement of indium incorporation efficiency for InGaN∕GaN quantum wells due to the rough, faceted surface of the GaN template grown in situ by ammonia-molecular-beam epitaxy is reported. The InGaN∕GaN quantum wells are grown by plasma-assisted molecular-beam epitaxy. Unlike the smooth (0002) surface of GaN template layers grown by metalorganic chemical vapor deposition, the surface of the template layers grown by ammonia-molecular-beam epitaxy is defined by {10-1m} pyramidal facets causing significant surface roughness. The drastically enhanced indium incorporation rate associated with the rough templates allows the InGaN∕GaN quantum wells to be grown at higher temperatures as it compensates for the increased thermal decomposition. High luminescence efficiency is achieved as a result. Using such efficient InGaN∕GaN quantum wells, light-emitting diodes have been grown entirely by molecular-beam epitaxy on sapphire substrates, demonstrating output power of 0.22mW for 20mA injection current.

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 score0.010
Threshold uncertainty score0.721

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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