Ion beam sputtering nanopatterning of thin metal films: the synergism of kinetic self-organization and coarsening
Bibliographic record
Abstract
Creation of self-organized surface nanostructures by ion beam sputtering (IBS) has strong potential for use in a broad range of technologies, from nanoelectronics and photonics to sensing and catalysis. Recently, we have developed a simple two-stage process for fabricating self-assembled arrays of Cu dots and lines on Si and SiO(2) substrates employing IBS of thin Cu films. We found that the self-assembled structures on the substrate result from a complex interaction between the structure-forming kinetic instability and various outcomes of the surface diffusion and coarsening, which tend to drive the surface pattern towards a thermodynamic equilibrium. Here, we analyze in detail the interplay of the kinetic nanopatterning and coarsening, in order to better understand the mechanisms defining the IBS-generated metallic structures on substrates of a different material. By means of kinetic Monte Carlo (KMC) modeling we investigate the pertinent trends of the self-organization at the surface of a metallic film. In the light of this discussion, we review the fabricated nanostructures. Finally, we present a KMC model of the two-stage IBS process and analyze the stability of the fabricated metal patterns at the surface of a substrate. We discuss the opportunities and challenges of this technique, concluding that the IBS creation of surface heterostructures provides considerable room for future numerical and experimental studies.
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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".