Effect of process conditions on the microstructural formation of dc reactively sputter deposited AlN
Bibliographic record
Abstract
Thin film aluminum nitride (AlN), because of its attractive properties, is a material with many applications. Its microstructure and hence properties are greatly influenced by the deposition process conditions. In this work, AlN was reactively deposited in a dc magnetron sputtering system at different proportions of nitrogen in the process gas mixture and at different process conditions. The microstructure and composition of the films were analyzed using x-ray diffraction data, energy dispersive spectroscopy, and scanning electron microscopy. Results show that for a process gas pressure of 0.67 Pa, a magnetron power of 100 W, and a substrate-target distance of 10 cm, a near stoichiometeric AlN can be prepared at nitrogen proportions as low as 20%. At these process conditions, (002) was the preferred crystal orientation. Dense fibrous structures were obtained, especially at low deposition rates with high proportions of nitrogen. Increase in magnetron power and decrease in distance result in a more porous structure. High kinetic energies (average) of the sputtered Al particles and high deposition rates tend to favor AlN(101) formation, while low kinetic energies of the Al particles and low deposition rates generally favor more of the AlN(100) formation.
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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 | 0.001 |
| 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".