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Record W1650498563 · doi:10.1116/1.1481044

Plasma etching of SiC surface using NF3

2002· article· en· W1650498563 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
FundersCanadian Bee Research Fund
KeywordsEtching (microfabrication)Reactive-ion etchingSputteringScanning electron microscopePlasma etchingDry etchingMaterials scienceVolumetric flow rateAtomic force microscopyAnalytical Chemistry (journal)PlasmaIsotropic etchingChemistryNanotechnologyComposite materialThin filmChromatography

Abstract

fetched live from OpenAlex

NF 3 was applied in the reactive ion etching of SiC. The effects of rf power and NF3 pressure on the etching rate and the surface morphology were investigated by means of scanning electron microscopy and atomic force microscopy. A procedure for getting the smooth and residue-free etched surface of SiC with a high etching rate of 87 nm/min was obtained under the conditions such as rf power of 100 W and NF3 pressure ranging from 0.5 to 1 Pa. A rough surface with spikes was obtained under the NF3 pressures higher than 3 Pa. It was found that the repetitive alternating treatment for the spike-formed and rough surface with the down flow etching using NF3 and Ar plasma sputtering enables us to obtain the smooth surface within the scale of ∼300 nm.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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