Sputtering from ion-beam-roughened Cu surfaces
Bibliographic record
Abstract
A comprehensive theoretical and experimental study of sputtering from copper surfaces roughened by low-energy ${\mathrm{Ar}}^{+}$ ion bombardment is reported. The total sputtering yields of thermally deposited Cu samples bombarded by 400-eV and 800-eV ions at 0\ifmmode^\circ\else\textdegree\fi{}--70\ifmmode^\circ\else\textdegree\fi{} angles of incidence have been measured and compared with a numerical model we have developed. To compute sputtering yields from rough surfaces, an original approach has been introduced, which accounts for sputtering anisotropy and shadowing of material emitted at grazing angles. The approach is flexible with respect to surface morphology and can be applied with any submicron structures. To specify the morphology that develops on the Cu surface under low-energy ion bombardment, the surface of bombarded Cu samples has been investigated by scanning electron microscopy. The morphology has been found highly unstable, appearing with random roughening, inclined conelike structures, ripples, or almost flat surfaces, depending on the bombardment conditions. For the samples considered it is found that the angular dependency of the total sputtering yield is strongly affected by surface morphology, which varies with the angle of ion incidence and bombardment energy. Approximations for accounting for the surface roughness required to describe sputtering at particular energy and angular regimes are discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".