Structural properties of InAs nanocrystals formed by sequential implantation of In and As ions in the Si (100) matrix
Bibliographic record
Abstract
The structural properties of InAs nanocrystals formed in Si by sequential implantation of In and As ions are studied in detail. We use a combination of x-ray diffraction, Rutherford backscattering spectroscopy, channeling, and transmission electron microscopy analyses to demonstrate that, regardless of the order in which ion species are implanted, InAs nanocrystals can be produced in Si (100) by means of sequential ion implantation complemented by subsequent thermal annealing. Whichever the order of implantation is, the nanocrystals are facetted and terminated by (111) planes, the epitaxial relationship being cube-on-cube, (100)InAs‖(100)Si with [001]InAs‖[001]Si, for most InAs nanocrystals. The size distribution of nanocrystals is much affected by the sequence of implantation. With As ions implanted first, nanocrystals of different sizes are concentrated within one and the same layer under the sample’s surface. In contrast, when In ions are implanted first, nanocrystals of different sizes are produced separately in space, forming a three-layer structure. As a consequence, inverting the order of implantation entails considerable changes in the morphology of the InAs nanocrystals obtained, affecting both their concentration profile and their size distribution. The result has a significant impact on the optical properties of the resulting samples.
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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) | 0.001 | 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".