Binary Phase Diagrams and Thermodynamic Properties of Silicon and Essential Doping Elements (Al, As, B, Bi, Ga, In, N, P, Sb and Tl)
Bibliographic record
Abstract
Fabrication of solar and electronic silicon wafers involves direct contact between solid, liquid and gas phases at near equilibrium conditions. Understanding of the phase diagrams and thermochemical properties of the Si-dopant binary systems is essential for providing processing conditions and for understanding the phase formation and transformation. In this work, ten Si-based binary phase diagrams, including Si with group IIIA elements (Al, B, Ga, In and Tl) and with group VA elements (As, Bi, N, P and Sb), have been reviewed. Each of these systems has been critically discussed on both aspects of phase diagram and thermodynamic properties. The available experimental data and thermodynamic parameters in the literature have been summarized and assessed thoroughly to provide consistent understanding of each system. Some systems were re-calculated to obtain a combination of the best evaluated phase diagram and a set of optimized thermodynamic parameters. As doping levels of solar and electronic silicon are of high technological importance, diffusion data has been presented to serve as a useful reference on the properties, behavior and quantities of metal impurities in silicon. This paper is meant to bridge the theoretical understanding of phase diagrams with the research and development of solar-grade silicon production, relying on the available information in the literature and our own analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".