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Record W2079237969 · doi:10.1116/1.3222874

Effect of magnetic field strength on deposition rate and energy flux in a dc magnetron sputtering system

2009· article· en· W2079237969 on OpenAlexaff
Samuel D. Ekpe, F. Jiménez, David Field, Martin J. Davis, S. K. Dew

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2009
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSputter depositionMagnetic fieldCavity magnetronMagnetSubstrate (aquarium)Materials sciencePlasmaFlux (metallurgy)Deposition (geology)SputteringElectron temperatureCondensed matter physicsThin filmPhysicsNanotechnologyMetallurgyNuclear physics

Abstract

fetched live from OpenAlex

Variations in the magnetic field strongly affect the plasma parameters in a magnetron sputtering system. This in turn affects the throughput as well as the energy flux to the substrate. The variation in the magnetic field in this study, for a dc magnetron process, is achieved by shifting the magnet assembly slightly away from the target. Measurements of the plasma parameters show that while the electron density at the substrate increases with decrease in magnetic field, the electron temperature decreases. The cooling of the electron temperature is consistent with results reported elsewhere. The deposition rate per input magnetron power is found to increase slightly with the decrease in magnetic field for the process conditions considered in this study. Results suggest that the energy flux to the substrate tends to show a general decrease with the shift in the magnet assembly.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.193
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2009
Admission routes1
Has abstractyes

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