DEPOSITION AND CHARACTERIZATION OF MAGNETRON CO-SPUTTERED InAlN FILM AT DIFFERENT Ar:N<sub>2</sub> GAS FLOW RATIOS
Bibliographic record
Abstract
This work presents the influence of changing Ar:N2 gas ratio on the growth and properties of InAlN films. InAlN films were deposited on [Formula: see text]-type Si(111) substrates by using magnetron co-sputtering method in 6:12, 10:10, 12:8 and 12:6 Ar:N2 mixtures at 300[Formula: see text]C. The surface, structural, electrical and optical properties of the deposited films were evaluated at different Ar:N2 ratios. The grain size and film thickness were increased by increasing the Ar flow with respect to N2. Structural characterization by X-ray diffraction (XRD) revealed an improvement in the crystalline quality of the [Formula: see text]-axis-oriented InAlN film by adjusting the Ar:N2 ratio to 12:8, however no diffraction peak corresponding to InAlN was detected at 6:12 Ar:N2 mixture. The surface roughness of InAlN film exhibited an increasing trend whereas the electrical resistivity of the film was decreased by increasing the Ar:N2 ratio. The bandgap of InAlN film was calculated from the optical reflectance spectra and it was found to change by changing the Ar:N2 gas ratio. The analysis of results from this work shows that the InAlN film with improved physical properties can be obtained through reactive magnetron co-sputtering method by adjusting the Ar:N2mixture to 12:8.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".