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Record W2533475650 · doi:10.1109/jphotov.2016.2618608

Passivation Effects on Low-Temperature Gettering in Multicrystalline Silicon

2016· article· en· W2533475650 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIEEE Journal of Photovoltaics · 2016
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
FundersInstitute of Gender and HealthEngineering and Physical Sciences Research CouncilUniversity of WarwickRoyal SocietyRoyal Academy of Engineering
KeywordsPassivationMaterials scienceSilicon nitrideCarrier lifetimeSiliconAnnealing (glass)ImpurityGetterNitrideOptoelectronicsMetallurgyNanotechnologyChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.203
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it