Measured Anomalous Dispersion, Kerr Comb, and Lasing in Hybrid TeO<sub>2</sub>-Coated Si<sub>3</sub>N<sub>4</sub> Waveguides
Bibliographic record
Abstract
We report measured anomalous dispersion, Kerr comb generation, and lasing on hybrid waveguides based on a standard wafer scale 400-nm Si3N4coated with the TeO2film. The use of thin Si3N4ensures reduced stress and film crack and therefore fabrication of scalable and high yield waveguides through conventional CMOS process. On the other hand, the highly nonlinear TeO2film is added to enhance nonlinearity and engineer waveguide dispersion while also acting as a host of rare-earth dopants for amplification and lasing. Experimental results are presented showing that the normal dispersion of 400 nm-thick Si3N4waveguides can be engineered to anomalous by adding the TeO2film. For a 1.6-μm wide, 500 μm bend radius ring resonator with a 424-nm thick TeO2coating, dispersion values of ~25 and ~78 ps/nm•km was measured at 1552 nm for the TE and TM-modes, respectively. Also, by exploiting the rare-earth solubility of the TeO2film and depositing an Er-doped TeO2coating, a microdisk laser was observed at pump power of 12 mW. These results show a promising route to potentially monolithic integration of linear, nonlinear and active functionalities in a single photonic chip.
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".