Contamination of silicon by iron at temperatures below 800 °C
Bibliographic record
Abstract
Abstract Iron‐related defects are deleterious in silicon‐based integrated circuits and photovoltaics, ruining devices and acting as strong recombination centres. Unless great care is taken, iron contamination will result from high temperature processing and so it is essential to understand the degree to which this can occur. Iron solubility data above ∼800 °C have been summarised by Istratov et al. (Appl. Phys. A 69, 13 (1999)), but many processes are performed at lower temperatures for which solubility data are scarce. We have studied iron contamination below ∼800 °C. Iron concentrations in intention‐ ally contaminated air‐annealed Czochralski silicon samples were determined from the change in minority carrier lifetime due to photodissociation of FeB pairs measured by quasi‐steady‐state photoconductance. In the ∼600 to 800 °C temperature range the iron concentration was found to vary according to $ 1.3 \times 10^{21} \; {\rm exp} \;\left (‐ { {1.8\;{\rm eV} } \over {kT} } \right)\;{\rm cm}^{‐3}. It is therefore the case that significantly more iron can dissolve in silicon at these temperatures than extrapolation of higher temperature data suggests, with the enhancement being by a factor of >20 at 600 °C. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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) | 0.002 | 0.001 |
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".