Comprehensive computer model for magnetron sputtering. I. Gas heating and rarefaction
Bibliographic record
Abstract
The complex interaction between several variables in magnetron sputtering discharges is a challenge in developing engineering design tools for industrial applications. For instance, at high pressures, rarefaction and gas heating should no longer be neglected for determining several parameters of the process. In this article, we use a comprehensive 3D reactor-scale simulator that incorporates most phenomena of interest in a self-consistent manner to simulate the transport of sputtered particles over a wide range of pressures and powers. Calculations of aluminum deposition rates and metal vapor densities are in reasonable agreement with experiments over a wide range of pressures and powers. Of the elements investigated (Al, Ti, and Cu), copper showed the greatest rarefaction (30%) due to its higher sputtering yield. Titanium, despite a slightly lower sputtering yield than Al, shows a greater rarefaction than aluminum as more particles are reflected from the target as high energy neutrals. In this case, a more efficient energy transfer process is responsible for the higher rarefaction observed in Ti sputtering when compared to Al. The authors also observed that by sputtering at a higher pressure, the probability of electron impact ionization of sputtered particles is increased and speculate about the role of this process in contrast to penning ionization, which is believed to be the dominant ionization mechanism in magnetron sputtering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".