Sum and difference frequency generation as diagnostics for leaky eigenmodes in two-dimensional photonic crystal waveguides
Bibliographic record
Abstract
We experimentally demonstrate how sum frequency generation (SFG) and difference frequency generation (DFG) of 150fs pulses can be enhanced and serve as diagnostics for leaky eigenmodes in a two-dimensional GaAs photonic crystal waveguide. SFG at 795nm is obtained in reflection from s-polarized 1900nm and p-polarized 1360nm pulses, with both input beams coupled to leaky eigenmodes; the SFG is enhanced by >350× compared to that from an untextured GaAs surface. We are able to detect Drude induced subnanometer blueshifts of the SFG, corresponding to refractive index changes of ⩽10−3. DFG of 1360nm light obtained in reflection from s-polarized 1900nm and p-polarized 793nm pulses displays an enhancement of >500× via three different leaky eigenmodes. As the 793nm beam polarization is varied from p polarized to right and left circularly polarized, the DFG remains essentially linearly polarized but with a reduced, albeit different, intensity for right and left circularly polarized 795nm pulses. Futhermore, the plane of polarization also rotates by different amounts for the left and right circularly polarized light, demonstrating interference of the components generated by s- and p-polarized 793nm pulses. Overall, our results demonstrate how enhanced DFG and SFG from leaky eigenmodes can be used to characterize their properties more precisely than linear optical techniques.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".