DC voltage fields generated by RF plasmas and their influence on film growth morphology through static attraction to metal wetting layers: Beyond ion bombardment effects
Bibliographic record
Abstract
It is shown that attractive electrostatic interactions between regions of positive charge in RF plasmas and the negative charge of metal wetting layers, present during compound semiconductor film growth, can have a greater influence than substrate temperature on film morphology. Using GaN and InN film growth as examples, the DC field component of a remote RF plasma is demonstrated to electrostatically affect metal wetting layers to the point of actually determining the mode of film growth. Examples of enhanced self-seeded nanopillar growth are provided in the case where the substrate is directly exposed to the DC field generated by the plasma. In another case, we show that electrostatic shielding of the DC field from the substrate can result in the growth of Ga-face GaN layers from gallium metal wetting layers at 490 °C with root-mean-square roughness values as low as 0.6 nm. This study has been carried out using a migration enhanced deposition technique with pulsed delivery of the metal precursor allowing the identification of metal wetting layers versus metal droplets as a function of the quantity of metal source delivered per cycle. It is also shown that electrostatic interactions with the plasma can affect metal rich growth limits, causing metal droplet formation for lower metal flux than would otherwise occur. Accordingly, film growth rates can be increased when shielding the substrate from the positive charge region of the plasma. For the example shown here, growth rates were more than doubled using a shielding grid.
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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) | 0.000 | 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".