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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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 (MRRs) 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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
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