MétaCan
Menu
Back to cohort
Record W1661535817 · doi:10.1063/1.3141690

Modeling Etching Plasmas: Needs and Challenges in Atomic and Molecular Data

2009· article· en· W1661535817 on OpenAlexafffund
J. Margot, Luc Stafford, Jean-Sébastien Poirier, Pierre‐Marc Bérubé, Mohamed Chaker, Jun Yan

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsEtching (microfabrication)PlasmaPlasma etchingMaterials sciencePlasma chemistryComputer scienceNanotechnologyEngineering physicsOptoelectronicsPhysicsNuclear physics

Abstract

fetched live from OpenAlex

This paper reviews works of the team to characterize and model chlorine high‐density plasmas. The model allows in particular determining the pressure‐dependence of the concentration of neutral and charged species. Comparison of this model to experimental measurements achieved in high‐density surface‐wave‐produced plasmas shows an excellent agreement for the neutral atomic and molecular species. As far as charged species are concerned, the model reproduces well experiments for atomic chlorine ions and electrons, but some discrepancy occurs for molecular positive ions and negative ions at low pressure. The cause of this discrepancy remains to be clarified but might result from an underestimation of the creation rates of Cl2+ and Cl−. The model seems promising for predicting the ion density in a recently installed ICP reactor.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.001
Research integrity0.0020.002
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 head0.056
GPT teacher head0.245
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicPlasma Diagnostics and ApplicationsFrench-language works237,207