Subwavelength Dispersion Engineering for Nonlinear Integrated Photonics
Bibliographic record
Abstract
The significant index contrast between silicon and other CMOS compatible materials provides substantial obstruction and difficulty in obtaining phase-matching conditions to compensate for the high nonlinear losses in silicon materials. This thesis demonstrates exploiting subwavelength grating structures to perform dispersion engineering on an integrated photonic platform to obtain phase-matching conditions between different material platforms to obtain third-order nonlinear optical processes to obtain frequency generation through Kerr combs. Different material platforms were implemented to obtain various phase-matching conditions with the final architecture based on an aluminum oxide bus waveguide coupled to a silicon nitride subwavelength grating structure to induce Kerr comb generation on robust integrated photonic architectures. These contrasting material platforms were successfully designed in order to eliminate the detrimental two-photon absorption, thereby reducing nonlinear losses. The final architecture consisted of the required phase-matchingconditions and coupling length for complete power transfer between the fundamental TE mode from the aluminum oxide bus waveguide to the second order TE mode in the silicon nitride SWG ring resonator, which obtained a high-quality factor of 126900 to obtain a high-Q integrated photonic devices using numerical 2D and 3D solver which are compatible with CMOS fabrication to obtain bright soliton Kerr combs that were simulated using the Lugiato-Lefever equation while eliminate the occurrence of nonlinear losses in the TPA effect.
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) | 0.003 | 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".