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Record W4324143088 · doi:10.3390/ma16062319

Improved Crystallinity of Annealed 0002 AlN Films on Sapphire Substrate

2023· article· en· W4324143088 on OpenAlexfundno aff
Bruno Comis Bersch, Tomàs Caminal Ros, Vegard Tollefsen, Erik Johannessen, Agnė Johannessen

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
FundersNorges ForskningsrådRéseau de cancérologie Rossy
KeywordsCrystallinityMaterials scienceSapphireSubstrate (aquarium)Annealing (glass)Composite materialOptoelectronicsChemical engineeringOptics

Abstract

fetched live from OpenAlex

AlN is a piezoelectric material used in telecommunication applications due to its high surface acoustic wave (SAW) velocity, stability, and mechanical strength. Its performance is linked to film quality, and one method to achieve high-quality films goes through the process of annealing. Consequently, c-orientated AlN film with a thickness of 1.1 μm deposited on sapphire was annealed at temperatures of 1100 °C and 1150 °C in a N2 controlled atmosphere. This was compared to annealing at 1100 °C, 1450 °C, and 1700 °C with N2 flow in an open atmosphere environment. Sample rotation studies revealed a significant impact on the ⍵-2θ X-ray rocking curve. A slight variation in the film crystallinity across the wafer was observed. After the annealing, it was found that the lattice parameter c was increased by up to 2%, whereas the screw dislocation density dropped from 3.31 × 1010 to 0.478 × 1010 cm−2, and the full width at half maximum (FWHM) of reflection (0002) was reduced from 1.16° to 0.41° at 1450 °C. It was shown that annealing in a N2-controlled atmosphere plays a major role in reducing the oxidation of the AlN film, which is important for acoustic wave devices where the electrodes are placed directly on the piezoelectric substrate. The face-to-face arrangement of the samples could further reduce this oxidation effect.

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 categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score0.995

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)0.0060.000

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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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