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Record W1985222544 · doi:10.1063/1.4914955

Two-dimensional X-ray diffraction and transmission electron microscopy study on the effect of magnetron sputtering atmosphere on GaN/SiC interface and gallium nitride thin film crystal structure

2015· article· en· W1985222544 on OpenAlexafffund
Huaxiang Shen, Guo‐zhen Zhu, Gianluigi A. Botton, Adrian Kitai

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAmorphous solidThin filmWurtzite crystal structureSputteringSputter depositionGallium nitrideTransmission electron microscopyOptoelectronicsSubstrate (aquarium)SiliconLayer (electronics)CrystallographyComposite materialNanotechnologyMetallurgyChemistryZinc

Abstract

fetched live from OpenAlex

The growth mechanisms of high quality GaN thin films on 6H-SiC by sputtering were investigated by X-ray diffraction (XRD) and scanning transmission electron microscopy (STEM). The XRD θ-2θ scans show that high quality (0002) oriented GaN was deposited on 6H-SiC by reactive magnetron sputtering. Pole figures obtained by 2D-XRD clarify that GaN thin films are dominated by (0002) oriented wurtzite GaN and {111} oriented zinc-blende GaN. A thin amorphous silicon oxide layer on SiC surfaces observed by STEM plays a critical role in terms of the orientation information transfer from the substrate to the GaN epilayer. The addition of H2 into Ar and/or N2 during sputtering can reduce the thickness of the amorphous layer. Moreover, adding 5% H2 into Ar can facilitate a phase transformation from amorphous to crystalline in the silicon oxide layer and eliminate the unwanted {33¯02} orientation in the GaN thin film. Fiber texture GaN thin films can be grown by adding 10% H2 into N2 due to the complex reaction between H2 and N2.

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 score0.038
Threshold uncertainty score0.595

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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