Numerical and Experimental Study of Forced Mixing with Static Magnetic Field on SiGe System
Bibliographic record
Abstract
A combined numerical and experimental investigation has been undertaken to explore the benefits of an applied static magnetic field on Silicon transport into a Germanium melt. This work utilized a similar material configuration to that used in the Liquid Phase Diffusion (LPD) and Melt-Replenishment Czochralski (Cz) growth systems. The measured concentration profiles from the samples processed with and without the application of magnetic field showed very similar shape. The amount of silicon transport into the melt is slightly higher in the samples processed under magnetic field, and there is a substantial difference in dissolution interface shape indicating a change in flow structure. Without magnetic field, a flat stable interface is observed. In the presence of an applied field, however, the dissolution interface remains flat in the center but dramatically curves back into the source material near the wall. This indicates a far higher dissolution rate at the edge of the silicon source. The 3-D numerical simulation results verify these observations and show that the flow structure of the melt has dramatically changed under the effect of magnetic field. Magnetic field-driven circulation cells develop at the edge of the melt increasing dissolution from this area. The change in the flow field is reflected in the concentration field, and consequently the transport of silicon into the melt is increased along the crucible wall.
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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".