Matching Layers in Bragg Reflection Waveguides for Enhanced Nonlinear Interaction
Bibliographic record
Abstract
Bragg reflection waveguides (BRWs) with matching layers placed between the core and claddings are proposed and analyzed as a means of enhancing the effective second-order optical nonlinearity. The addition of matching layers to conventional BRW structures provides extra degrees of freedom, which enable further optimization to enhance the nonlinear conversion efficiency. Pertinent parameters including overlap between modal profiles of the interacting waves, the group velocity mismatch and group velocity dispersion are examined. The structures studied in this work are designed using the GaAs-AlxGa1-xAs material system. It is shown that, in comparison to phase-matched BRWs with no matching layers, the proposed structure benefits from relaxing the constraint over the phase-matched core thickness. For typical designs, it is shown that the new structure can provide over an order of magnitude enhancement in nonlinear coupling efficiency and 30% decrease in group velocity mismatch. It is also shown that the group velocity dispersion of the first and second harmonics are reduced by 10% and 52%, respectively.
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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 | 0.001 |
| Open science | 0.001 | 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".