Cooperative roles of chemical reactions and mechanical friction in chemical mechanical polishing of gallium nitride assisted by OH radicals: tight-binding quantum chemical molecular dynamics simulations
Bibliographic record
Abstract
Chemical mechanical polishing (CMP) is a key manufacturing process for applying gallium nitride (GaN), especially the Ga-face GaN, to semiconductor devices such as laser diodes. However, the CMP efficiency for GaN is very low due to its high hardness and chemical stability. Experimentally, OH radicals appear able to improve the CMP efficiency of GaN polished by a SiO2 abrasive grain, whereas the mechanisms of the OH-radical-assisted CMP process remain unclear because experimental elucidation of the complex chemical reactions occurring among GaN substrate, abrasive grain, and OH radicals is difficult. In this work, we used our previously developed tight-binding quantum chemical molecular dynamics simulator to study the OH-radical-assisted CMP process of the widely employed Ga-face GaN substrate polished by an amorphous SiO2 abrasive grain in an effort to understand how OH radicals assist the CMP process and then aid the development of next-generation CMP techniques. Our simulations revealed that the OH-radical-assisted CMP process of GaN occurs via the following three basic reaction steps: (i) first, all hydrogen terminations on the GaN surface are replaced by OH terminations through continuous reactions with OH radicals; (ii) after the substrate is fully terminated by OH, the hydrogen atoms of these OH terminations are removed by reacting with newly added OH radicals, which forms H2O molecules and leaves energetic oxygen atoms with dangling bonds on the surface; and (iii) finally, these energetic oxygen atoms intrude inside the substrate with concomitant dissociation of Ga-N bonds and the generation of N2 and gallium hydroxide molecules, which accumulatively lead to the removal of N and Ga atoms from the substrate.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".