Modeling the suppression of boron diffusion in Si∕SiGe due to carbon incorporation
Bibliographic record
Abstract
We used the process simulator FLOOPS-ISE to implement a consistent model describing the diffusion behaviors of boron and carbon in silicon and silicon germanium. In particular, our model successfully accounts for boron and carbon behaviors in a wide range of sample structures and experimental conditions over the complete temperature range of 750–1070°C in inert and oxidizing ambients, and in the presence of implant damage. The structures studied include cases where the boron and carbon profiles are separated as well as cases where profiles overlap, cases with carbon in silicon and in SiGe, and our own recent experiments where boron diffusion within a SiGeC region has been characterized. We model carbon diffusion by the kickout and Frank-Turnbull mechanisms, and interstitial capture by substitutional carbon, and demonstrate that a model must incorporate all three effects to satisfactorily explain published data. We also include standard models for boron-interstitial clusters and {311} defects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".