Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
Bibliographic record
Abstract
We report on low-temperature and low-pressure deposition conditions of 140 °C and 1.5 mTorr, respectively, to achieve high-optical quality silicon nitride thin films. We deposit the silicon nitride films using an electron cyclotron resonance plasma-enhanced chemical vapour deposition (ECR-PECVD) chamber with Ar-diluted SiH4, and N2 gas. Variable-angle spectroscopic ellipsometry was used to determine the thickness and refractive index of the silicon nitride films, which ranged from 300 to 650 nm and 1.8 to 2.1 at 638 nm, respectively. We used Rutherford backscattering spectrometry to determine the chemical composition of the films, including oxygen contamination, and elastic recoil detection to characterize the removal of hydrogen after annealing. The as-deposited films are found to have variable relative silicon and nitrogen compositions with significant oxygen content and hydrogen incorporation of 10–20 and 17–21%, respectively. Atomic force microscopy measurements show a decrease in root mean square roughness after annealing for a variety of films. Prism coupling measurements show losses as low as 1.3, 0.3 and 1.5 ± 0.1 dB/cm at 638, 980 and 1550 nm, respectively, without the need for post-process annealing. Based on this study, we find that the as-deposited ECR-PECVD SiOxNy:Hz films have a suitable thickness, refractive index and optical loss for their use in visible and near-infrared integrated photonic devices.
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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) | 0.001 | 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 | 0.001 | 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".