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Record W2010599919 · doi:10.1088/1674-1056/20/3/038102

Origin of high oxide to nitride polishing selectivity of ceria-based slurry in the presence of picolinic acid

2011· article· en· W2010599919 on OpenAlexfundno aff
Liangyong Wang, Bo Liu, Zhitang Song, Weili Liu, Songlin Feng, Huang David, S. V. Babu

Bibliographic record

VenueChinese Physics B · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPicolinic acidMaterials scienceZeta potentialSlurryOxidePolishingSelectivityThermogravimetric analysisNitrideChemical engineeringInorganic chemistryMetallurgyChemistryNanoparticleComposite materialNanotechnologyOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

We report on the investigation of the origin of high oxide to nitride polishing selectivity of ceria-based slurry in the presence of picolinic acid. The oxide to nitride removal selectivity of the ceria slurry with picolinic acid is as high as 76.6 in the chemical mechanical polishing. By using zeta potential analyzer, particle size analyzer, horizon profilometer, thermogravimetric analysis and Fourier transform infrared spectroscopy, the pre- and the post-polished wafer surfaces as well as the pre- and the post-used ceria-based slurries are compared. Possible mechanism of high oxide to nitride selectivity with using ceria-based slurry with picolinic acid is discussed.

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 score0.001

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)0.0000.000

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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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