Ultrathin oxynitride formation by low energy ion implantation
Bibliographic record
Abstract
Oxynitride films have been formed by rapid thermal processing of N-implanted wafers. The formation mechanism, the chemical composition, and the physical thickness of the oxynitride films were studied by x-ray photoelectron spectroscopy (XPS). Segregation of nitrogen to the surface was performed on nitrogen ion-implant wafers under nitrogen gas. The outcome of the segregation is the formation of an ultrathin oxynitride layer ranging in thickness from 4 to 8.5 Å . Oxidation of nitrogen ion-implanted wafers, where the nitrogen is segregated beforehand and nitrogen ion-implanted wafers with no segregation prior to oxidation, is the focus of this study. XPS results showed that simultaneous segregation and oxidation forms an oxynitride film consisting of two layers where the interface is rich in nitrogen and the surface in oxygen. In the case of nitrogen segregation prior to oxidation, the nitrogen atoms in the oxynitride film, formed at oxidation temperatures less than 1000 °C, are uniformly distributed throughout the film in the form of SiOxNy. At high temperatures (∼1100 °C), the composition of the oxynitride formed by simultaneous segregation and oxidation becomes similar to that where N-segregation is performed prior to oxidation. The presence of nitrogen atoms retards significantly the diffusion of oxygen to the substrate surface thus producing uniform ultrathin films ranging in thickness from 13 to 40 Å.
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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) | 0.001 | 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".