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Record W2133896634 · doi:10.1063/1.4933037

Light-induced degradation of native silicon oxide–silicon nitride bilayer passivated silicon

2015· article· en· W2133896634 on OpenAlexaff
Zahidur Chowdhury, Nazir P. Kherani

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassivationMaterials scienceSilicon nitrideSiliconOxideSilicon oxideDegradation (telecommunications)Plasma-enhanced chemical vapor depositionBilayerOptoelectronicsLayer (electronics)NanotechnologyChemistryElectronic engineeringMetallurgy

Abstract

fetched live from OpenAlex

This article reports on the effects of aging and light induced degradation of the passivation quality of the interface formed by the crystalline silicon surface and facile grown oxide—SiNx bilayer. Stable passivation quality against aging and light soaking require thicker oxide layers grown at room temperature, suggesting that thicker oxide layers mitigate the migration of hydrogen from the interface and hence the defect density under light soaking. In addition, the stoichiometry of the PECVD SiNx influences the stability of the passivation quality. Specifically, the rate of degradation in passivation quality is observed to correlate with the optical absorption properties of SiNx; the higher the optical absorption the greater the degradation in passivation. This result is attributed to neutralization of the K+ centers in SiNx. Passivation layers with SiNx deposited with 5% silane in nitrogen to ammonia gas ratio of 7 and facile grown native oxide thickness of ∼1 nm resulted in the most stable passivation scheme within the scope of the reported experiments.

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)0.000
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 score0.004
Threshold uncertainty score1.000

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 head0.025
GPT teacher head0.225
Teacher spread0.201 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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