Octave-spanning supercontinuum generation in a CMOS-compatible thin Si<sub>3</sub>N<sub>4</sub> waveguide coated with highly nonlinear TeO<sub>2</sub>
Bibliographic record
Abstract
Supercontinuum generation (SCG) is an important nonlinear optical process enabling broadband light sources for many applications, for which silicon nitride (Si 3 N 4 ) has emerged as a leading on-chip platform. To achieve suitable group velocity dispersion and high confinement for broadband SCG the Si 3 N 4 waveguide layer used is typically thick (>∼700 nm), which can lead to high stress and cracks unless specialized processing steps are used. Here, we report on efficient octave-spanning SCG in a thinner moderate-confinement 400-nm Si 3 N 4 platform using a highly nonlinear tellurium oxide (TeO 2 ) coating. An octave supercontinuum spanning from 0.89 to 2.11 µm is achieved at a low peak power of 258 W using a 100-fs laser centered at 1565 nm. Our numerical simulations agree well with the experimental results giving a nonlinear parameter of 2.5 ± 0.5 W −1 m −1 , an increase by a factor of 2.5, when coating the Si 3 N 4 waveguide with a TeO 2 film. This work demonstrates highly efficient SCG via effective dispersion engineering and an enhanced nonlinearity in CMOS-compatible hybrid TeO 2 –Si 3 N 4 waveguides and a promising route to monolithically integrated nonlinear, linear, and active functionalities on a single silicon 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) | 0.000 | 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".