Challenges in Large-Area Multi-Wafer SiC Epitaxy for Production Needs
Bibliographic record
Abstract
The rapid market development for SiC-devices during the last years can be attributed particularly to the success in supplying high-quality SiC wafers and corresponding epitaxial layers. The device quality could be enhanced and the costs were reduced by enlarging the wafer size as well as by a significant progress in epitaxial growth of active layers by using multi-wafer CVD systems. In this paper we want to give an overview of CVD multi-wafer systems used for SiC growth in the past and today. We present recent results of SiC homoepitaxial growth using our multi-wafer hot-wall CVD system. This equipment exhibits a capacity of 5×3” wafers per run and can be upgraded to a 7×3” or 5×4” setup. By optimizing the process conditions epitaxial layers with excellent crystal quality, purity and homogeneity of doping and thickness have been grown. Issues like reproducibility, drift of parameters and system stability over several runs will be discussed.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".