Impact of Grating Duty-Cycle Randomness on DFB Laser Performance
Bibliographic record
Abstract
The duty-cycle randomness (DCR) of the Bragg grating of the distributed feedback (DFB) lasers introduced by the fabrication process is inevitable, even with state-of-the-art technologies such as electron beam lithography and dry or wet etching.This work investigates the impact of grating DCR on DFB laser performance through numerical simulations. The result reveals that such randomness causes a reduction in the side mode suppression ratio (SMSR), and deteriorates the noise characteristics, i.e., broadens the linewidth and increases the relative intensity noise (RIN). With the grating DCR, the effective grating coupling coefficient decreases as evidenced by the reduced Bragg stopband width. However, the longitudinal spatial hole burning (LSHB) effect in the DFB lasers can somewhat be diminished by the grating DCR. The seriousness of these effects depends on different grating structures and their coupling strengths. Our simulation shows that a degradation of 17 dB can be brought to the SMSR of the uniform grating DFB lasers with their duty cycles taking a deviation of ±25% in a uniformly distributed random fashion. It also broadens the linewidth of the quarter-wavelength phase-shifted DFB lasers by more than 2.5 folds. The impact of this effect on the RIN is moderate—less than 2%. All the performance deteriorations can partially be attributed to the effective reduction in the grating coupling coefficient of around 20% by such a DCR.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".