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Record W2139089734 · doi:10.1149/2.004204jss

Copper CMP: The Relationship between Polish Rate Uniformity and Lubrication

2012· article· en· W2139089734 on OpenAlexafffund
Luis Nolan, Ken Cadien

Bibliographic record

VenueECS Journal of Solid State Science and Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWaferLubricationPolishingMaterials scienceChemical-mechanical planarizationCopperEnhanced Data Rates for GSM EvolutionSlurryComposite materialMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

Chemical Mechanical Polishing of Copper (Cu-CMP) is an important yet poorly-understood nanofabrication technique. In this work, we demonstrate that the degree of non-uniformity in polishing rates, described by the new quantity MRRNU , relates to the lubrication conditions of the polishing couple. MRRNU is the difference between the highest and lowest material removal rates ( MRR s) recorded across the wafer surface, normalized by the average MRR . The polish rate non-uniformity that this quantity encapsulates is shown to transition from negative (wafer-scale dishing) to positive (wafer-scale doming) with increasing Sommerfeld number, for the pad and slurry chemistry used here. This is explained by the presence of co-existing lubrication zones in the pad-wafer interface. Each of the zones described, namely the edge zone , hydrodynamic zone and suppression zone , demonstrate a different relationship between pressure and removal rate, resulting in variation in MRR across the wafer.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

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Same venueECS Journal of Solid State Science and TechnologySame topicAdvanced Surface Polishing TechniquesFrench-language works237,207