Percolation and ripening in Si<sub>1-x</sub>Ge<sub>x</sub>/Si(001) islands: effect of misfit strain
Bibliographic record
Abstract
We study the island size distributions of Xi1-xGex/Si(001) (x equals 0.4 - .07) islands of varying Ge fractions and thicknesses by ultrahigh vacuum chemical vapor deposition. The island size distributions of the percolating islands obey a dynamic scaling hypothesis admitting only one length scale governing the growth, in the limit of large island sizes. Although bimodal distributions are found in coherent islands at large misfit strain, due to the large stress concentration at island perimeters; faulted dislocation loops forming as islands grow remove this stress concentration. This re-establishes a unimodal distribution,, reclaiming the scaling hypothesis. We show that the misfit strain is renormalized and, thus, is not essential in determining the size distribution. We also demonstrate evidence for Smoluchowski ripening mechanism occuring during growth. Finally, we discuss implications of these issues on achieving a uniform Xi1-xGex/Si(001) island distribution, which is crucial for technological applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".