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Record W2003502359 · doi:10.1063/1.2949278

Study of tungsten filament aging in hot-wire chemical vapor deposition with silacyclobutane as a source gas and the H2 etching effect

2008· article· en· W2003502359 on OpenAlexafffund
Ling Tong, Chris E. Sveen, Yujun Shi

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemical vapor depositionAuger electron spectroscopySiliconTungstenScanning electron microscopeEtching (microfabrication)Analytical Chemistry (journal)Materials scienceProtein filamentChemistryMetallurgyComposite materialNanotechnology

Abstract

fetched live from OpenAlex

The tungsten filament aging when using silacyclobutane (SCB) as a source gas in a hot-wire chemical vapor deposition reactor was systematically studied by the characterization of surface morphology using scanning electron microscopy and the chemical composition analysis of the filament surfaces using Auger electron spectroscopy. It is shown that filament aging involves the formation of silicides and under more severe conditions, a pure silicon deposit. At low pressures of SCB samples, e.g., 0.06 and 0.03Torr, only Si3W5 alloy was formed. Silicon-rich silicide, Si2W, was found when using a higher pressure of SCB at 0.12Torr. At the high SCB pressure of 0.12Torr and low temperatures, pure silicon was deposited on the W filament surface. It is also demonstrated that H2 can etch the aged filament at high temperatures above 1900°C. The etching products detected by the 10.5eV vacuum ultraviolet laser single photon ionization∕time-of-flight mass spectrometer include SiH4, SiCHx (x=2–5), and SiC2Hy (y=4–7).

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 score0.047
Threshold uncertainty score0.309

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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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

Citations14
Published2008
Admission routes2
Has abstractyes

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