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Record W2110072607 · doi:10.1149/2.016406jss

High Rate Chemical Mechanical Polishing of Boron-Doped Polycrystalline Silicon

2014· article· en· W2110072607 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.

Bibliographic record

VenueECS Journal of Solid State Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceBoronChemical-mechanical planarizationPolishingDopingPolycrystalline siliconSiliconCrystalliteMetallurgyComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

In this paper, the effect of lubrication on the polish rate and the surface quality of highly boron-doped polysilicon is presented. The mechanical effects of polishing were studied by altering the CMP process and lubrication regime. It was shown that increasing the polish velocity and slurry flow rate does not always increase the polish rate and that the lubrication behavior plays a dominant role in this process. Increasing the mechanical forces and altering the lubrication regime to boundary and dry lubrication were the key factors which resulted in a maximum polish rate. The maximum polish rate for the boron-doped polysilicon with 3.4 mΩ.cm −1 resistivity was ∼0.75μ/min and achieved in the dry lubrication regime. The coefficient of friction (COF) was also studied and agreed with the polish rate values where the dry lubrication polishing showed highest COF following the Stribeck curve. Finally the surface quality of the polished wafers were studied by AFM and it was shown that the lubrication behavior and friction force had minor effect on the surface roughness and all the wafers were perfectly smooth after polishing (R q ∼2Å).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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