Hard AlN films prepared by low duty cycle magnetron sputtering and by other deposition techniques
Bibliographic record
Abstract
Crystalline AlN films are very attractive due to their properties such as high thermal stability and relatively high hardness and piezoelectric response. However, the deposition of dense textured AlN films with superior quality at a high deposition rate remains a challenge. In the present work, a reactive low duty cycle pulsed direct current magnetron sputtering (LDMS) process was employed to deposit AlN films on glass and silicon substrates. An arc-free discharge on the Al target was achieved by using short voltage pulses of 10 μs at a low duty cycle of 10%. The authors optimized the deposition conditions in terms of reactive gas flow, working pressure, average target power, substrate temperature, substrate bias, and the level of target erosion. With the optimized deposition conditions, the authors were able to obtain transparent crystalline AlN films with strong (002) preferential orientation and very good optical and mechanical properties: The AlN films with the highest refractive index of 2.1 present a hardness of up to 22 GPa and a low residual stress of ≈+300 MPa. Meanwhile, a relatively high deposition rate of ≈45 nm/min was achieved. A systematic comparison of the LDMS process with five other magnetron sputtering approaches working at optimized conditions indicated superior performance of the LDMS technique. This approach leads to the most promising results in terms of discharge stability, deposition rate, and film properties, and thus, it shows much promise for reactive deposition of dielectric materials and hard optical coatings.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".