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Record W2908098933 · doi:10.1063/1.5083776

Beam current stability improvements of negative carbon ions extraction from a multi-cusp ion source

2018· article· en· W2908098933 on OpenAlexfundno aff
Stéphane Melanson, David Potkins, Hamish McDonald, Chris Philpott, Morgan Dehnel

Bibliographic record

VenueAIP conference proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsIon sourceMaterials scienceBeam (structure)Ion beamIon beam depositionIon gunIonAtomic physicsPerveanceCurrent (fluid)Aperture (computer memory)Cathode rayElectrodeElectronOpticsChemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Ion implantation requires a high beam current stability to ensure a uniform implantation dose across the wafers. This requires ion sources with a stable extraction system to achieve a high beam current stability. Our goal is to extract 0.5 mA of C2- with less than 10 glitches per hour, with a glitch defined as any variation greater than +/- 5% of the nominal beam current. The beam energy should be between 10 keV and 30 keV while the normalized 4 RMS emittance should be less than 1 mm·mrad. We’ve shown that high negative carbon ion current densities could be obtained with a multi-cusp ion source when acetylene was used as the feed gas (0.27 mA of C2-), but there was significant sparking between the electrodes, which led to frequent beam current glitches (>1000/hour). In this study, we investigate the different factors that contribute to the sparking between the electrodes. We found that the sparking is highly correlated to the dumping of the co-extracted electrons and the beam strike on the electrodes. Extraction simulations were completed to determine how the electron dumping can be improved and how the beam strikes on the electrodes can be reduced. Furthermore, we modified the magnetic configuration in the plasma chamber to decrease the electron density close to the extraction aperture, which reduced the co-extracted electron current by a factor of almost 5 and reduced sparking frequency to about 20 glitches per hour. However, there was a corresponding decrease in the extracted beam current due to the smaller aperture sizes needed to reduce the sparking, with only 0.05 mA of C2-.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.720

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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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