Numerical Analysis of the Combined Influence of Accelerated Crucible Rotation and Dynamic Crucible Translation on Liquid Phase Diffusion Growth of SiGe
Bibliographic record
Abstract
The effects of accelerated crucible rotation technique (ACRT) and dynamic translation on liquid phase diffusion (LPD) growth of SixGe1−x single crystals have been separately investigated numerically in earlier works and were found to have a very positive impact on the LPD growth process. Building upon these findings, in this paper, we study the consequences of imposing both ACRT and dynamic translation on this growth technique. Time-dependent, axisymmetric numerical simulations using moving grid approach have been carried out using finite volume code Ansys Fluent. Crucible translation effect is simulated using dynamic thermal boundary condition. Results are compared to the case in which this growth system is subjected to ACRT only. It is predicted that by combining ACRT with dynamic pulling, excellent axial compositional uniformity can be achieved and growth rate can be improved substantially without significantly compromising on the benefits of employing ACRT. The results show that it is advantageous to utilize the combination of ACRT and dynamic translation during LPD growth rather than using them independently for producing relatively uniform composition SixGe1−x single crystals in a shorter span of time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".