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Record W1981582146 · doi:10.1063/1.4798928

The augmented saddle field discharge characteristics and its applications for plasma enhanced chemical vapour deposition

2013· article· en· W1981582146 on OpenAlexafffund
Johnson Wong, Davit Yeghikyan, Nazir P. Kherani

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSiliconSilanePlasmaSubstrate (aquarium)Chemical vapor depositionAnodePlasma-enhanced chemical vapor depositionAmorphous siliconWaferOptoelectronicsAtomic physicsAnalytical Chemistry (journal)ElectrodeCrystalline siliconChemistryComposite material

Abstract

fetched live from OpenAlex

A high ion flux parallel electrode plasma is proposed and studied in its DC configuration. By cascading a diode source region which supplies electrons and a saddle field region where these seed electrons are energized and amplified, the energy of ion bombardment on the substrate can be decoupled from the plasma density. The sufficiently large density of electrons and holes in the vicinity of the substrate raises the possibility to perform plasma enhanced chemical vapour deposition on insulating materials, at low sheath voltages (around 40 V in the configuration studied), at low temperatures in which the surface mobility of film growth species may be provided by the bombardment of moderate energy ions. As a benchmarking exercise, experiments are carried out on silane discharge characteristics and deposition of hydrogenated amorphous silicon (a-Si:H) on both silicon wafer and glass. The films grown at low anode voltages have excellent microstructures with predominantly monohydride bonds, sharp band tails, but relatively high integrated defect density in the mid 1016/cm3 range for the particular substrate temperature of 180 °C, indicating that further optimizations are necessary if the electrode configuration is to be used to create a-Si:H devices.

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 score0.132
Threshold uncertainty score0.321

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 head0.007
GPT teacher head0.199
Teacher spread0.193 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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