Modeling and Optimization of Quasi-Phase Matching Via Domain-Disordering
Bibliographic record
Abstract
Second-harmonic generation in a quasi-phase matched waveguide produced using a domain-disordered GaAs-AlAs superlattice is modeled including the effects of group velocity mismatch, nonlinear refraction, two-photon absorption, and linear loss. The model predicts our experimentally observed second-harmonic powers within an order of magnitude. Self-phase modulation and two-photon absorption led to reduced conversion efficiencies of up to 33% at input peak powers >50 W. Group velocity mismatch results in a reduction of 23% in conversion efficiency using estimated group velocities calculated from the measured effective refractive index. The modeling also shows that the conversion efficiency peaks at propagation lengths longer than the pulse walk off length and that duty cycle variations induced shifts in the tuning curves. Group velocity mismatch also increased the conversion bandwidth by ~ 30%. Incomplete modulation of chi(2)in disordered regions reduced the output conversion efficiency by up to two orders of magnitude. Grating-assisted phase matching led to a 7% efficiency drop for a Deltan of 0.045 at the second-harmonic and 0.01 at the fundamental. This model serves as a valuable tool to provide insight into the optimization of these devices.
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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 | 0.001 | 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".