The effect of oxide precipitates on minority carrier lifetime in <i>n</i>-type silicon
Bibliographic record
Abstract
Supersaturated levels of interstitial oxygen in Czochralski silicon can lead to the formation of oxide precipitates. Although beneficial from an internal gettering perspective, oxygen-related extended defects give rise to recombination which reduces minority carrier lifetime. The highest efficiency silicon solar cells are made from n-type substrates in which oxide precipitates can have a detrimental impact on cell efficiency. In order to quantify and to understand the mechanism of recombination in such materials, we correlate injection level-dependent minority carrier lifetime data measured with silicon nitride surface passivation with interstitial oxygen loss and precipitate concentration measurements in samples processed under substantially different conditions. We account for surface recombination, doping level, and precipitate morphology to present a generalised parameterisation of lifetime. The lifetime data are analysed in terms of recombination activity which is dependent on precipitate density or on the surface area of different morphologies of precipitates. Correlation of the lifetime data with interstitial oxygen loss data shows that the recombination activity is likely to be dependent on the precipitate surface area. We generalise our findings to estimate the impact of oxide precipitates with a given surface area on lifetime in both n-type and p-type silicon.
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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) | 0.000 | 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) | 0.000 | 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".