Seed Production and Melt Replenishment for the Czochralski Growth of Silicon Germanium
Bibliographic record
Abstract
The silicon transport in a silicongermanium melt has been studied to address the issues of melt replenishment and seed production for the Czochralski growth of silicon germanium (SiGe) crystals.The growth of SiGe single crystals by the Czochralski method requires that the melt be replenished with silicon during the growth process due to the rejection of germanium into the melt during solidication.To facilitate the replenishment of the melt, an accurate knowledge of the dissolution rate of silicon into the melt and its transport through the melt is required.To address these issues, a number of experiments have been carried out on the dissolution of silicon into a germanium melt.Liquid phase diusion growth experiments were also conducted for insight into transport and as a possible method for seed crystal production.The experiments encompassed various temperatures, crucible geometries, crucible translation, and magnetic eld levels to determine optimum conditions for the most favorable dissolution rates and mass transport in the melt.Results have shown that replenishment from bottom of the crucible is most eective due to the enhanced silicon transport by buoyancy.The application of magnetic elds may also provide an eective mean to control the replenishment rate (mass transport rate) in the melt.
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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 | 0.000 | 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".