MétaCan
Menu
Back to cohort
Record W1972606339 · doi:10.14356/kona.2003020

Slurry Design for Chemical Mechanical Polishing

2003· article· en· W1972606339 on OpenAlex
G. Bahar Basim, Brij M. Moudgil

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.

Bibliographic record

VenueKONA Powder and Particle Journal · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsInstitute of Particle Physics
FundersUniversity of FloridaNational Science Foundation
KeywordsChemical-mechanical planarizationSlurryMaterials sciencePolishingWaferAbrasiveMicroelectronicsParticle (ecology)Abrasive machiningSubstrate (aquarium)DissolutionComposite materialNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

Chemical Mechanical Polishing (CMP) process is widely used in the microelectronics industry for planarization of metal and dielectric layers to achieve multi-layer metallization. For an effective polishing, it is necessary to minimize the surface defects while attaining a good planarity with optimal material removal rate. These requirements can be met by controlling the chemical and mechanical interactions during the polishing process, or in other words, by engineering the slurry chemistry, particulate properties and stability. This paper reviews the impact of chemical, inter-particle and pad-particle-substrate interactions on CMP performance. It is shown that for consistently high performing slurries, stability of abrasive particles must be achieved under the dynamic processing conditions by providing sufficient pad-particle-wafer interactions.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.478
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.266
Teacher spread0.234 · 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