Self-organized metal networks at ion-etched Cu∕Si and Ag∕Si interfaces
Bibliographic record
Abstract
We report self-organized metal nanopatterns on Si substrates produced by ion beam etching. We have deposited thin layers of metal such as Cu or Ag on Si substrates and then etched the deposited layers by a 1–5keV Ar+ ion beam at room temperature. At the stage when the metal-Si interface is reached, we have observed networks of metal clusters on the Si substrate with the characteristic size of 30–60nm for Cu and 100–200nm for Ag. The Cu patterns are sensitive to the ion energy. At 1keV energy, we observe a well-defined Cu network, whereas at 3–5keV energy, the Cu pattern becomes fuzzy without clear boundaries. To systematize and explain our results, we have suggested a kinetic model that combines ion etching and coarsening of the metal clusters on Si substrates. From our kinetic Monte Carlo simulations, we have found that the cooperative effect of coarsening and etching has a regime when the size of metal clusters can be approximated by the expression a(4D∕aR)1∕3, where D is the surface diffusivity of metal adatoms on the Si substrate, R is the etch rate, and a is the interatomic distance. Our synergistic model of coarsening and sputtering explains the observed difference in Cu and Ag cluster sizes and predicts the fuzzy Cu patterns at elevated ion energies.
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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) | 0.000 | 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".