TEOS-PECVD Films for High-Quality SiO<sub>2</sub> Cladding Layers in Si<sub>3</sub>N<sub>4</sub>-Photonics with Low Mechanical Stress and Optical Loss
Bibliographic record
Abstract
In this paper, we developed a multistep approach for depositing silicon dioxide (SiO2) cladding layers using TEOS-based PECVD. We deposited the SiO2layer in multiple thin steps, separated by annealing time at 1000°C, to gradually increase the thickness and avoid cracking. Our results showed that the SiO2films deposited under different processes had the same stress, and refractive index behavior, as long as the thickness of each step is below a critical thickness (500nm on Si substrate and 300nm on SiO2 substrate); hence, we were able to achieve high coverage and low mechanical stress (-200 MPa) in the deposited SiO2films. RBS results demonstrated the high quality of our TEOS-based oxide without any contamination, contrary to the nitrogen contamination that exists through the whole thickness of the silane-based SiO2films. To demonstrate the effectiveness of our TEOS-PECVD process, we integrated the SiO2cladding layers with silicon nitride (Si3N4) waveguides and the stability of their optical responses at 1550 nm under various annealing steps was confirmed. Our results showed that the SiO2cladding layers provided effective optical confinement, with optical losses around 0.67 dB/cm suitable for high-performance waveguides operating at 1550 nm.
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".