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Record W4306836125 · doi:10.3390/coatings12101581

Downstream Electric Field Effects during Film Deposition with a Radio Frequency Plasma and Observations of Carbon Reduction

2022· article· en· W4306836125 on OpenAlexaff
Kenneth Butcher, Vasil Georgiev, Dimka Georgieva, Rositsa Gergova, Penka Terziyska, Peter W. Binsted

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsLakehead University
Fundersnot available
KeywordsBiasingPlasmaAnalytical Chemistry (journal)Substrate (aquarium)Materials scienceElectric fieldOptoelectronicsChemistryVoltagePhysics

Abstract

fetched live from OpenAlex

Strong electric fields are generated by radio frequency (RF) plasma sources, and though the RF portion is too high a frequency for ions to react, the direct current (DC) portion of these fields has been shown to cause the atomic migration of metals, which can influence film morphology even downstream of the plasma where ionized plasma species are absent. In particular, we have observed the growth of nanopillars due to metal atoms migrating toward the positive field of the remote plasma. A biased grid placed between the plasma and the substrate can shield the substrate from these fields so that, when grounded, smooth films can be grown to a root mean square roughness of less than 1 nm. Positively biasing the grid returns the growth of nanocolumns. Interestingly, negatively biasing the grid significantly reduced the carbon and hydrocarbon content of gallium nitride films grown at a low temperature (~660 °C) using a nitrogen plasma, as observed using secondary ion mass spectroscopy (SIMS) and optical absorption measurements. The films also showed a notable improvement in conductivity and visible appearance.

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 score0.012
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 head0.194
Teacher spread0.188 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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