The Effect of Slurry Properties on the CMP Removal Rate of Boron Doped Polysilicon
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".