A kinetic model for the oxidation of silicon germanium alloys
Bibliographic record
Abstract
We propose a complete model for the oxidation of silicon germanium. Our model includes the participation of both silicon and germanium atoms in the oxidation process and the replacement by silicon of germanium in mixed oxides. Our model is capable of predicting, as a function of time, the oxide thickness, the profile of the silicon in the underlying alloy, and the profile of germanium in the oxide. The parameters of the model vary with temperature, alloy composition, and oxidizing ambient. The model shows excellent agreement with published results, with model parameters following trends consistent with the physical phenomena hypothesized. The presence of germanium catalyzes both the silicon and the germanium oxidation rates, and all reaction rates increase with increasing temperature. The resulting effective oxidation rate is enhanced, with respect to the oxidation of pure silicon, at all germanium concentrations. Mixed oxides form only in the case of high germanium concentrations, but at high temperatures the rapid growth of a thick oxide results in a slowing of oxidant diffusion, and the oxide composition switches back to a pure silicon oxide.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".