Inhomogeneous rarefaction of the process gas in a direct current magnetron sputtering system
Bibliographic record
Abstract
The interactions between energetic particles and the sputter gas in a magnetron sputtering system have strong effects on the growth, structures, and properties of the film. These interactions result in inhomogeneous rarefaction of the gas in the space between target and substrate and affect both the transport of particles towards the substrate and the dynamics of the plasma. A hybrid Monte Carlo and fluid model is developed to simulate three-dimensional (3D) gas rarefaction due to the sputtering of metals in argon, neon, and krypton. The governing equations are solved iteratively in a 3D space with a nonuniform grid (octree). Collision events between the sputtered particles and the process gas are assumed as the dominant source of gas heating; however, the effect of the reflected neutrals is also included in the model. Gas rarefaction profiles have been predicted for different process conditions. Model results compare well with experimental ones. The extent of rarefaction depends on process conditions as well as the thermal conductivity of the gas. Materials with high sputtering yield, such as silver, show more rarefaction at a given cathode current than those with low sputtering yield, such as tungsten and aluminum. A higher sputtering yield means more sputtered atoms, thus more energy and momentum deposited in the gas. For a 75mm target at 300W and 10mTorr, a rarefaction of about 65% is obtained for the sputtering of Al in Ar gas, with the substrate plane located 10cm in front of the target.
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 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".