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Record W4252209894 · doi:10.1149/09702.0059ecst

Influence of Different Carbon Precursors on Optical and Electrical Properties of Silicon Carbonitride Thin Films

2020· article· en· W4252209894 on OpenAlexaff
Aysegul Abdelal, Zahra Khatami, Peter Mascher

Bibliographic record

VenueECS Transactions · 2020
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of New BrunswickMcMaster University
Fundersnot available
KeywordsThin filmPlasma-enhanced chemical vapor depositionCarbon filmMaterials scienceElectron cyclotron resonanceSiliconAmorphous carbonNanoindentationAcetyleneChemical vapor depositionAnalytical Chemistry (journal)Carbon fibersRefractive indexAmorphous siliconMethaneHydrogenAmorphous solidChemical engineeringPlasmaComposite materialNanotechnologyChemistryOptoelectronicsCrystalline siliconOrganic chemistry

Abstract

fetched live from OpenAlex

We investigate the thin film properties of the amorphous hydrogenated silicon carbonitride (a-SiCN:H) deposited by the electron cyclotron resonance plasma enhanced chemical vapor deposition (ECR PECVD) technique. The elemental composition, film density, and complex refractive index of the SiCN films were analyzed as functions of hydrocarbon precursors’ (acetylene (C 2 H 2 ) or methane (CH 4 )) flow rates. The difference in the reactivity and hydrogen content of the carbon precursors influence the thin film properties. The highest breakdown voltage obtained from our thin films was 29 V. Preliminary results of nanoindentation of the thin films are also presented in order to compare the hardness performance of the thin films obtained by different carbon sources.

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 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 score0.002
Threshold uncertainty score0.412

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 head0.018
GPT teacher head0.232
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2020
Admission routes1
Has abstractyes

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