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Record W2075158943 · doi:10.1063/1.4764011

Optimal hydrogenated amorphous silicon/silicon nitride bilayer passivation of <i>n</i>-type crystalline silicon using response surface methodology

2012· article· en· W2075158943 on OpenAlexaff
Dmitri Stepanov, Nazir P. Kherani

Bibliographic record

VenueApplied Physics Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassivationDangling bondMaterials scienceSilicon nitrideSiliconAmorphous siliconBilayerCrystalline siliconChemical vapor depositionNanocrystalline siliconDeposition (geology)Amorphous solidAnalytical Chemistry (journal)OptoelectronicsNanotechnologyLayer (electronics)ChemistryCrystallographyMembrane

Abstract

fetched live from OpenAlex

This study reports the highest quality surface passivation achieved with hydrogenated amorphous silicon and amorphous silicon nitride (SiNx) bilayer stack deposited using plasma enhanced chemical vapour deposition on 1–2 Ωcm n-type crystalline silicon. The SiNx deposition conditions were investigated using response surface methodology (RSM). Optimized deposition parameters obtained from the RSM study yielded a low surface recombination velocity (SRV) of 3.5 cm/s. Interface defect and charge densities, inferred using the interface dangling bond recombination model, revealed a strong influence of charge on the SRV reduction. The model predicts a lower SRV of 1.5 cm/s for the bilayer passivation scheme.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.259
Teacher spread0.214 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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