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Record W2094649173 · doi:10.1116/1.1651544

Optical and compositional characterization of SiOxNy and SiOx thin films deposited by electron cyclotron resonance plasma enhanced chemical vapor deposition

2004· article· en· W2094649173 on OpenAlexaff
Tyler Roschuk, J. Wójcik, Xin Tan, J. A. Davies, Peter Mascher

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2004
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElastic recoil detectionSilicon oxynitrideElectron cyclotron resonanceAnalytical Chemistry (journal)Fourier transform infrared spectroscopyThin filmChemical vapor depositionEllipsometrySiliconPlasma-enhanced chemical vapor depositionMaterials scienceSilicon oxideDeposition (geology)ChemistryIonOpticsSilicon nitrideNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

Thin silicon oxynitride (SiOxNy) and silicon-rich silicon-oxide (SiOx,x⩽2) films of varying composition have been deposited by electron cyclotron resonance plasma enhanced chemical vapor deposition. Films were deposited using various source gas flow rates while maintaining a constant chamber pressure. Thicknesses and refractive indices for these films were determined using ellipsometry. Bonding of the constituent atoms was analyzed using Fourier transform infrared (FTIR) spectroscopy. FTIR spectroscopy also allowed for the detection of bonded species such as hydrogen. Compositional characteristics were determined using various forms of ion beam analysis such as Rutherford backscattering and elastic recoil detection. These analysis techniques were used to determine the values of x and y, the molar fractions of oxygen and nitrogen, respectively, and the total amount of hydrogen present in the films. Using the results obtained from these methods the film characteristics were determined as a function of the deposition conditions.

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 score0.015
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations15
Published2004
Admission routes1
Has abstractyes

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