Strain-driven diffusion process during silicon oxidation investigated by coupling density functional theory and activation relaxation technique
Bibliographic record
Abstract
The reaction of oxygen molecules on an oxidized silicon model-substrate is investigated using an efficient potential energy hypersurface exploration that provides a rich picture of the associated energy landscape, energy barriers, and insertion mechanisms. Oxygen molecules are brought in, one by one, onto an oxidized silicon substrate, and accurate pathways for sublayer oxidation are identified through the coupling of density functional theory to the activation relaxation technique nouveau, an open-ended unbiased reaction pathway searching method, allowing full exploration of potential energy surface. We show that strain energy increases with O coverage, driving the kinetics of diffusion at the Si/SiO2 interface in the interfacial layer and deeper into the bulk: at low coverage, interface reconstruction dominates while at high coverage, oxygen diffusion at the interface or even deeper into the bottom layers is favored. A changing trend in energetics is observed that favors atomic diffusions to occur at high coverage while they appear to be unlikely at low coverage. Upon increasing coverage, strain is accumulated at the interface, allowing the oxygen atom to diffuse as the strain becomes large enough. The observed atomic diffusion at the interface releases the accumulated strain, which is consistent with a layer-by-layer oxidation growth.
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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 | 0.000 |
| 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) | 0.000 | 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".