Mathematical Modeling of Material Removal Rate in Sapphire CMP using Silica Particle
Bibliographic record
Abstract
In this research, we aim to define a mathematical model for the material removal rate (MRR) in the chemical mechanical polishing (CMP) of sapphire.Silica slurries with particle sizes of 4, 20, 55, and 105 nm were used as abrasives in the polishing tests.The results showed that the material removal rate increased with abrasive particle size from 20 to 105 nm.However, we found that 4 nm silica particles produced a remarkably high removal rate, comparable to that achieved using 105 nm silica particles.These results indicated that the MRR is governed by dominant factors related to the abrasive particle size.To further investigate this relationship, the chemical and mechanical contributions of silica particles were analyzed based on the experimental data.A predictive model for the material removal rate in sapphire CMP was then derived.The comparison between the predicted MRR values and experimental results showed that the maximum error decreased from 20% to 3.33% as the abrasive size increased from 4 nm to 105 nm.The results in this research may indicate the MRR of sapphire CMP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".