Bibliographic record
Abstract
Current nanofabrication techniques allow patterning of optical waveguides with submicron cores. This results in strong confinement of light, which leads to high optical intensities. If the waveguides are fabricated with materials that display a large nonlinear Kerr coefficient, then nonlinear optical phenomena can take place in a very efficient manner. Silicon nitride is a very well-studied material in the electronics industry. The material has a large transparency window, from the ultraviolet to the short-wave infrared, and its fabrication is completely compatible with standard techniques formerly developed by the semiconductor industry. Silicon nitride strip waveguides can also confine light, and diverse applications based on nonlinear optics have been demonstrated before. However, these applications required core thickness above 300 nm and they are very challenging to fabricate in a reliable manner with standard deposition techniques. In this thesis, we have studied unconventional silicon nitride waveguides that are more robust for fabrication. The first layout corresponds to a thin strip waveguide with low optical confinement and propagation losses of only 6 dB/m. This technology was originally developed at the University of California, Santa Barbara. We used the technology to demonstrate wavelength conversion of high-speed data. In this thesis, we developed another silicon nitride technology that allowed for high light confinement. We discovered that by modifying the stoichiometry of the film during the deposition process, one could drastically change the optical and mechanical properties of the material. With this technology we demonstrated octave-spanning supercontinuum generation in collaboration with the Technical University of Denmark and XPM-based all-optical processing in collaboration with McGill University. These results indicate that this platform is very suitable for nonlinear integrated optics. The long-term goal of our research is being able to attain an optical parametric amplifier on chip using a continuous-wave pump laser source. In this thesis we benchmarked the losses of high-confinement waveguides for the realization of 10 dB parametric net-gain on chip and identified silicon nitride as the most plausible technology to achieve this goal in the near future.
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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) | 0.000 | 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 | 0.001 |
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".