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Record W2423410833 · doi:10.1116/1.4943921

Focused gas beam injection for efficient ammonia-molecular beam epitaxial growth of III-nitride semiconductors

2016· article· en· W2423410833 on OpenAlexaff
Abderrahim Rahim Boucherif, Maxime Rondeau, Hubert Pelletier, Philippe‐Olivier Provost, Abderraouf Boucherif, Christian Dubuc, Hassan Maher, Richard Arès

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceMolecular beam epitaxyOptoelectronicsSubstrate (aquarium)NitrideLuminescencePhotoluminescenceCoaxialMolecular beamLayer (electronics)OpticsEpitaxyChemistryMoleculeNanotechnology

Abstract

fetched live from OpenAlex

A focused gas beam injection is proposed for high-efficiency ammonia molecular beam epitaxial growth of III-nitride. This new injector design is based on a double, coaxial radial high-conductance geometry, which allows rotation-free growth with fast gas switching. The injection profile is characterized through a mobile ion gauge and is then compared to simulations, where experimental results show that up to 27% of the injected molecules reach the surface of the substrate. The injector is tested for the growth of GaN layers, and high-resolution x-ray diffraction rocking curves of a 1 μm-thick GaN layer grown on a commercial GaN template (1 μm-thick layer of GaN on Si) was measured around the 002 Bragg condition and a full width at half maximum of 594 arc sec was obtained. Low-temperature photoluminescence for the same layer shows intense band edge emission and a low yellow luminescence. Hall measurements of the silicon-doped layers show high carrier concentrations up to 2 × 1019 cm−3 and a corresponding mobility of 204 cm2/V s.

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)0.002
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 score0.022
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicGaN-based semiconductor devices and materialsFrench-language works237,207