Flattop Efficient Cascaded χ$^{\bf {\bm (}2{\bm)}}$ (SFG + DFG)-Based Wideband Wavelength Converters Using Step-Chirped Gratings
Bibliographic record
Abstract
We numerically evaluate efficient wideband wavelength converters based on quasi-phase-matched cascaded sum and difference frequency generation (SFG + DFG) using step-chirped gratings (SCG) in lossy lithium niobate waveguides, and compare them to the ones using uniform gratings, assuming a large pump wavelength difference considering a full model of depleted pump and sum-frequency waves. For the same length, appropriate critical period shifts are presented for the number of sections and chirp steps to achieve flattop conversion efficiency responses with peak-to-peak ripples less than 0.2 dB. To obtain the maximum efficiency and flat response with a decreasing chirp step of 1 nm, the criteria for the design of optimum four-section single-pass and two-section double-pass SCG-based devices, including the assignment of length (to achieve a desired bandwidth) and pump power, are presented considering a 75-nm pump wavelength difference. Also, the performances of single-pass and double-pass schemes for the two- and four-section SCG-based devices are given, respectively, and compared to those of uniform grating-based devices with and without pump detuning, assuming a 3-cm-long waveguide and 50-mW input pump powers. For the same length and power, using the SCG with fixed pumps instead of the uniform grating with detuned pumps shows a noteworthy increase in the mean efficiency to attain almost the same response flatness and bandwidth.
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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 | 0.001 | 0.000 |
| 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".