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Record W2115756331 · doi:10.1139/cjp-2013-0558

Control of growth process for obtaining high-quality a-SiO:H

2014· article· en· W2115756331 on OpenAlexvenueno aff
Yasushi Sobajima, Shota Kinoshita, Shinnosuke Kakimoto, Ryoji Okumoto, Chitose Sada, Akihisa Matsuda, Hiroaki Okamoto

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDangling bondPlasmaDissociation (chemistry)OxygenMoleculeAnalytical Chemistry (journal)Growth rateAmorphous siliconSiliconPhysicsPhysical chemistryChemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Film-growth process of hydrogenated amorphous silicon–oxygen alloys (a-SiO:H) from CO2/(CO2 + SiH4) plasma has been investigated to control the optoelectronic properties in the resulting materials. Optical emission spectroscopy results and simple simulation results for steady-state density of chemical species in the plasma indicate that main film-growth precursors for a-SiO:H are SiH3, OH, and O. Si dangling-bond defect density is drastically increased in a-SiO:H when increasing the CO2 gas ratio in CO2/(CO2 + SiH4) plasma, being caused by the increase in the contribution ratio of Si-related short-lifetime species (SiHx, x < 2) to film growth owing to a severe SiH4-molecule depletion because of high-rate consumption reaction of SiH4 with O produced from CO2 in the plasma. Considering the primary electron impact dissociation reactions of source gas molecules and several secondary chemical reactions in the plasma, the guiding principle for obtaining high quality a-SiO:H has been proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.224
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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Same venueCanadian Journal of PhysicsSame topicThin-Film Transistor TechnologiesFrench-language works237,207