Three-dimensional modeling of SiGe by the traveling solvent method in the presence of magnetic field and rotating crucible
Bibliographic record
Abstract
A three-dimensional numerical simulation study was carried out for crystal growth of SiGe by the traveling solvent method (TSM). The Effects of magnetic field intensity and sample rotation on the transport structures (fluid flow, heat and mass transfer and concentraion) in the solvent were investigated in detail. The full stead-state Navier-Stokes equations, as well as the energy, mass transport and continuity equations were solved numerically using the finite element method. An external axial magnetic field is applied to suppress convection in the solvent. It was found that the intensity of the flow at the centre of the crucible decreases at a faster rate compared to the flow near the walls when increasing the magnetic field intensity. This behaviour creates a stable and uniform silicon distribution in the horizontal plane near the growth interface. The application of crucible rotation in the presence of the axial magnetic field is an attempt to control the buoyancy induced convection throughtout the solvent region. It was observed that in the application of rotation, there is no significant change to suppress the flow intensity. It was found that the rotating magnetic field (RMF) has a marked effect on the silicon concentration, changing it from convex to nearly flat when the magnetic field intensity increases. An alternative method for removing buoyancy convection from the melt is growing the crystal in a gravity-free environment. The microgravity (10-4g) is numerically situated and compared with the ground base condition.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".