MétaCan
Menu
Back to cohort
Record W2093335350 · doi:10.1063/1.4733336

High-quality surface passivation of silicon using native oxide and silicon nitride layers

2012· article· en· W2093335350 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Physics Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassivationMaterials scienceSilicon nitrideOxideSilicon oxideSiliconCarrier lifetimeLayer (electronics)Deposition (geology)Chemical vapor depositionOptoelectronicsNitrideRemote plasmaAnalytical Chemistry (journal)Chemical engineeringNanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

We report on the attainment of high quality surface passivation of crystalline silicon using facile native oxide and plasma enhanced chemical vapour deposition SiNx. Using systematic measurements of excess carrier density dependent minority carrier lifetime, it is observed that the inferred interface defect density decreases with increasing native oxide thickness while the interface charge density remains unchanged with thickness, which ranges from 0.2 Å to 10 Å. A surface recombination velocity of 8 cm/s is attained corresponding to a native oxide layer thickness of ∼10 Å. Similar chemically grown oxide layer followed by SiNx deposition is shown to yield comparable passivation, indicating practical viability of the 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.

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 score0.021
Threshold uncertainty score0.772

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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