Void evolution in silicon under inert and dry oxidizing ambient annealing and the role of a Si1−xGex epilayer cap
Bibliographic record
Abstract
Voids were formed in silicon (Si) and silicon germanium/silicon (Si1−xGex/Si) samples containing 5% or 9% Ge (at. %) by 30 keV, 5 × 1016 cm−2 helium (He+) implantation followed by annealing in nitrogen (N2) or dry oxygen (O2) atmospheres in the temperature range 960–1110 °C. Si1−xGex thicknesses were 60 nm and 20 nm for 5% and 9% Ge, respectively. He+ implantation energy was set such that in Si1−xGex/Si samples voids were formed inside the Si substrate. An increase in annealing temperature resulted in an increase in the average void diameter and decrease in the average void density. Due to the presence of implantation damage and the relatively high temperature anneals, Ge diffusion occurs, which results in a stress gradient in the sample that interacts with the void layer. The presence of Ge also results in weaker Si-Ge bonds (compared to Si-Si bonds). This leads to an increase in the rate of cavity migration providing a likely explanation for the increase in the average void diameter and decrease in the average void density in Si1−xGex/Si samples when compared to the similarly prepared Si samples. No impact on the void evolution process was observed as a result of changing the anneal atmosphere from N2 to dry O2.
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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".