Selective Epitaxy of Si/SiGe to Improve pMOS Devices by Recessed Source/Drain and/or Buried SiGe Channels
Bibliographic record
Abstract
Selective Epitaxial Growth of SiGe and/or Si-cap/SiGe heterostructures offer an elegant way to improve pMOS device performance. This paper discusses some important challenges and characteristics of the corresponding epi process. Loading effects are strongly reduced by choosing the growth conditions away from the mass transport regime, i.e. by reducing the growth pressure and/or increasing the gas velocity. Anomalous SiGe thickening at convex corners of recessed areas and the impact of the underlying SiGe on the growth behavior during Si- capping are discussed as well. The limits of the chemical and thermal budgets during pre-epi treatments as defined by the device concepts require some process optimization but are not a show stopper.
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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 | 0.000 | 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) | 0.000 | 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".