Ge dots and nanostructures grown epitaxially on Si
Bibliographic record
Abstract
We review recent progress in the growth and characterization of Si 1− x Ge x islands and Ge dots on (001) Si. We discuss the evolution of the island morphology with Si 1− x Ge x coverage, and the effect of growth parameters or post-growth annealing on the shape of islands and dots. We outline some of the structural, vibrational, and optical properties of Si 1− x Ge x islands and review recent advances in the determination of their composition and strain distribution. In particular, we present an analytical electron transmission microscopy study of the Ge spatial distribution in Ge dots and Si / Si 1− x Ge x island superlattices grown by molecular beam epitaxy and ultra-high vacuum chemical vapour deposition. We describe the use of undulated Si 1− x Ge x island superlattices for infrared detection at telecommunication wavelengths. Finally, we discuss various approaches currently being investigated to engineer Si 1− x Ge x quantum dots and, in particular, control their size, density, and spatial distribution. As examples, we show how C pre-deposition on Si(001) can influence nucleation and growth of Ge islands and how low temperature Si homo-epitaxy can lead to a particular surface cusp morphology that may promote dot nucleation.
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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 | 0.001 |
| 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".