Passivation Effects on Low-Temperature Gettering in Multicrystalline Silicon
Bibliographic record
Abstract
Annealing at ≤ 500 °C changes minority carrier lifetime in as-grown multicrystalline silicon substantially. Part of the change arises from internal gettering of impurities, but surface passivation for lifetime measurement results in additional effects. We report experiments that aim to clarify the role of passivation. Long-term annealing (up to 60 h) is performed on silicon nitride passivated multicrystalline silicon, and lifetime and interstitial iron concentrations are monitored at each processing stage. Lifetime in all samples is improved under certain conditions, with improvements always achieved at 400 °C. Increases are pronounced in low-lifetime bottom samples, with improvement by a factor of 2.7 at 400 °C or 3.8 at 500 °C. Important differences are found compared with our previous study with iodine-ethanol passivation. First, as-received lifetime is higher with silicon nitride not due to a substantial difference in surface recombination. Second, while interstitial iron concentrations often initially increase with iodine-ethanol, they tend to reduce with silicon nitride. Third, lifetime in high-lifetime samples reduces substantially with iodine-ethanol but increases with silicon nitride. Secondary ion mass spectrometry reveals high iron concentrations in annealed silicon nitride. Results are discussed in terms of gettering of impurities to, and bulk passivation arising from, silicon nitride films.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".