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Record W2001981329 · doi:10.1088/0957-0233/17/11/023

Electrical probe calibration and power calculation for a miniature 13.56 MHz plasma source

2006· article· en· W2001981329 on OpenAlexafffund
Valérie Léveillé, Sylvain Coulombe

Bibliographic record

VenueMeasurement Science and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCalibrationPlasmaPower (physics)Electrical engineeringNuclear engineeringPhysicsComputer scienceEngineeringNuclear physicsThermodynamics

Abstract

fetched live from OpenAlex

The paper highlights and provides solutions to the difficulties encountered with electrical probe measurements performed on a miniature, high capacitive impedance plasma device excited at 13.56 MHz. It is shown that a proper calibration of the phase angle between the circuit current and load voltage signals is required when commercial capacitive voltage probes (Tektronix P6139A and P5100 models) are used. A method to calculate the electrical characteristics of the plasma source and accounting for this calibration is described. The P6139A probe, which has the largest input capacitance and shortest cable length, introduced the smallest phase angle (−2 ± 1°), while the P5100 probe, which is used for higher input voltages and has a longer cable length, introduced a considerably larger phase angle (−34 ± 1°) for typical resistive loads. Simple circuit models are developed in an attempt to isolate the phase induced by the probe capacitance and cable length. The application of the proposed calibration to a miniature atmospheric pressure glow discharge source considerably reduced the error in the calculation of the power dissipated in the plasma device, though it remained relatively high (1 W ± 42%) due to the highly capacitive nature of the device.

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 score0.675
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations10
Published2006
Admission routes2
Has abstractyes

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