MétaCan
Menu
Back to cohort
Record W2469184644 · doi:10.1149/2.0151605jss

The Effect of Slurry Properties on the CMP Removal Rate of Boron Doped Polysilicon

2016· article· en· W2469184644 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 · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates - Technology Futures
KeywordsChemical-mechanical planarizationMaterials scienceSlurryAbrasiveDopingBoronWaferColloidal silicaComposite materialSaturation (graph theory)SiliconPolishingChemical engineeringMetallurgyNanotechnologyOptoelectronicsChemistryCoating

Abstract

fetched live from OpenAlex

Doped polysilicon is used as a via fill material for through silicon via technology. Boron doping is used to reduce the polysilicon resistivity but boron doping significantly decreases the polish removal rate. Here the influence of slurry characteristics, chemistry and abrasive properties, on the chemical mechanical polishing of heavily boron-doped polysilicon is investigated. The effect of slurry pH on silica abrasive size and colloidal stability is examined as well as the influence of these effects on the polish rate. The optimum abrasive concentration is ∼6 wt% and higher concentrations did not improve the polish rate due to the saturation of slurry particles on the wafer surface. Smaller abrasive particles, with 10 times higher surface area per unit weight improved the polish rate ∼20%. Finally, polish conditions with mechanical and chemical dominance are compared.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.237
Teacher spread0.230 · 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