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Record W2023928312 · doi:10.1063/1.4873169

Growth optimization and optical properties of AlGaNAs alloys

2014· article· en· W2023928312 on OpenAlexafffund
Gitanjali Kolhatkar, Abderraouf Boucherif, Christopher E. Valdivia, Steven G. Wallace, S. Fafard, Vincent Aimez, Richard Arès

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCyrium Technologies (Canada)Institut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBand gapMaterials sciencePhase (matter)Absorption (acoustics)TransmittanceMolecular beam epitaxyWide-bandgap semiconductorNitrideAnalytical Chemistry (journal)Direct and indirect band gapsEpitaxyOptoelectronicsNanotechnologyChemistryComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

The effect of Al on the surface morphology of chemical beam epitaxy grown AlGaNAs alloys is studied. Pits attributed to N clustering appearing on the dilute nitride surface become smaller, denser, and more uniformly distributed in the presence of Al. This reveals that the introduction of Al results in more homogenous N atoms spatial distribution. A growth temperature study reveals the formation of 3D structures at high temperature due to phase separation. The density of these structures decreases, while their diameter and height increase when the temperature is raised from 380 °C to 565 °C. At growth temperatures in the 380–420 °C range, the phase separation is suppressed and the growth mode is 2D. At 420 °C, the N incorporation is also maximized, making it the optimum temperature. The absorption coefficient and the bandgap of AlGaNAs alloys are extracted from transmittance measurement. A good agreement is obtained between the experimentally measured bandgap and the theoretical values calculated using the band anticrossing model. A bandgap as low as 1.22 eV was reached using Al and N concentrations of ∼15% and ∼3.4%, respectively.

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 score0.116
Threshold uncertainty score0.291

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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