Effect of unintentionally introduced oxygen on the electron–cyclotron resonance chemical-vapor deposition of SiNX films
Bibliographic record
Abstract
We establish the role of oxygen atoms on the structural, chemical, and mechanical properties of SiOXNY films grown on Si and InP substrates by electron–cyclotron resonance chemical-vapor deposition (ECR CVD) using a diluted SiH4 and N2 mixture in Ar, under controlled conditions. The mechanical and chemical properties of ECR-CVD SiNX films depend on the oxygen contamination even when this element is present in low concentrations. The compressive stress of SiNX films deposited with a low (and constant) content of oxygen (less than 12%) is shown to be in qualitative agreement with a model of repulsive Coulomb forces related mainly to polar N–H+− units in the SiNX network. We observe a decrease of the film compressive stress when the N2/SiH4 flow ratio increases, which is due to the increase of Si–N bonds in detriment of N–H bonds. Films deposited with high oxygen content in the plasma show a decrease of nitrogen incorporation. Oxygen radicals species compete with those of nitrogen in their reaction with silicon dangling bonds, which has as a consequence a decrease in the incorporation of nitrogen. Additional creation of oxygen radicals, with no hydrogen dilution, is more effective in decreasing the number of N–H bonds, or the compressive stress in the SiNX films, than the corresponding creation of nitrogen radicals. The mechanical properties of SiNX films contaminated with oxygen are controlled, in general, by the total number of both nitrogen plus oxygen atoms relative to silicon. The buffered HF (BHF) film etch rate is enhanced and thus is mainly controlled by the oxygen content. Low values of the compressive stress do not necessarily imply low values of BHF etch rate or a high N/Si ratio. We also present a discussion of the origin of the unintentional incorporation of oxygen in a ECR-CVD system designed for industrial production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".