The Effect of Crucible Rotation and Crucible Size in Top‐Seeded Solution Growth of Single‐Crystal Silicon Carbide
Bibliographic record
Abstract
Abstract The top‐seeded solution growth method is a promising technique for growing high‐quality silicon carbide single crystal. Some inherent issues in this growth process, such as morphological instability, polycrystalline growth, and low growth rate, should be clarified. A high temperature difference between the seed and the crucible wall in this system is needed to enhance growth. However, such a high temperature gradient makes the radial growth rate profile non‐uniform due to the effect of Marangoni convection below the seed crystal, which leads to poor crystal quality. In the present work, the effects of crucible size and crucible rotation are numerically investigated to minimize the effect of Marangoni convection. The possibilities of the occurrence of growth‐rate non‐uniformity and undesired impurity incorporation are examined. A smaller crucible (in radius) leads to a more uniform growth rate profile. However, it gives rise to a higher possibility of impurity incorporation. It is also predicted that crucible rotation is ineffective in suppressing the Marangoni flow near the seed edge. This leads to a flow stagnation in the center of the melt, and consequently, it does not enhance the carbon transport below the seed. It also does not reduce the possibility of undesired impurity incorporation.
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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 | 0.001 |
| 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".