Comparison of cascaded χ2 wavelength conversions in quasi-phase-matched (QPM) waveguides
Bibliographic record
Abstract
Cascaded second-order nonlinear interaction (&chi(2));based wavelength conversion technique has attracted much attention due to its unique characteristics such as low noise and broadband, which are critical in fiber communication networks. In this report, wavelength conversions based on the newly proposed SFG-DFG (sum frequency generation - difference frequency generation) and conventional SHG-DFG (second harmonic generation - difference frequency generation) are studied and compared both experimentally and theoretically in a LiNbO3 quasi-phase matched (QPM) waveguide. It is shown that the same conversion efficiency can be achieved by employing two pump sources with only half power each (P1, P2) in the SFG-DFG scheme as compared with the SHG-DFG scheme with a single higher power pump beam (P=P1+P2). It is shown that the cascaded SFG-DFG based wavelength conversion has a larger 3-dB pump tolerance bandwidth. The theoretical results are consistent well with the experimental ones. It is found that the pump wavelength difference can be separated by a span as large as 75 nm, while 3-dB signal conversion efficiency is retained in a 45 mm-long device. It is also exhibited that tolerance of temperature for the cascaded SFG-DFG remains the same as that of the cascaded SHG-DFG based devices. The results show that the SFG-DFG wavelength conversion scheme is very attractive for practical applications.
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 | 0.001 |
| Open science | 0.000 | 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".