Recent Advances in Hollow Cathode Technology for Plasma-Enhanced ALD—Plasma Surface Modifications for Aluminum and Stainless-Steel Cathodes
Bibliographic record
Abstract
Recent designs have allowed hollow cathode gas plasma sources to be adopted for use in plasma-enhanced atomic layer deposition with the benefit of lower oxygen contamination for non-oxide films (a brief review of this is provided). From a design perspective, the cathode metal is of particular interest since—for a given set of conditions—the metal work function should determine the density of electron emission that drives the hollow cathode effect. However, we found that relatively rapid surface modification of the metal cathodes in the first hour or more of operation has a stronger influence. Langmuir probe measurements and hollow cathode electrical characteristics were used to study nitrogen and oxygen plasma surface modification of aluminum and stainless-steel hollow cathodes. It was found that the nitridation and oxidation of these metal cathodes resulted in higher plasma densities, in some cases by more than an order of magnitude, and a wider range of pressure operation. Moreover, it was initially thought that the use of aluminum cathodes would not be practical for gas plasma applications, as aluminum is extremely soft and susceptible to sputtering; however, it was found that oxide and nitride modification of the surface could protect the cathodes from such problems, possibly making them viable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".