A Numerical Study for the Growth of InGaSb Crystals with a Flatter Interface by Vertical Gradient Freezing under Normal Gravity and Utilizing Bayesian Optimization
Bibliographic record
Abstract
Growth of InGaSb crystals with a flatter growth interface by Vertical Gradient Freezing (VFG) under normal gravity was numerically investigated. The growth of high-quality crystals is difficult due to the adverse effect of natural convection under the effect of Earth’s gravity. The natural convection developing in the melt leads to an undesirable non-flat growth interface and thus affects the quality of grown crystals. In order to obtain a flatter growth interface and better compositional uniformity in the melt, the applications of crucible rotation and external magnetic field are considered. We carried out a numerical study to optimize growth parameters for these purposes. We also considered the utilization of Bayesian optimization to have a fast search for most favorable control parameters. Such an optimization has led to a significant reduction in the growth interface deflection and a flatter growth interface was maintained during the entire growth process. Strength of natural convection in the melt was minimized and melt solute uniformity was improved. The growth rate was also increased without compromising crystal quality. Simulation results showed that the use of a numerical simulation together with Bayesian optimization is an effective way to provide a better control for the growth parameters involved.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".