Impact of Sidewall Roughness on Integrated Bragg Gratings
Bibliographic record
Abstract
A major issue in the fabrication of integrated Bragg grating (IBG) filters in highly confined waveguides is the average effective index fluctuations caused by sidewall roughness. In this work, we model the impact of this effect on IBG spectral responses and we identify key parameters that need to be controlled in order to minimize distortions. We show that only low spatial frequency components of the noise are relevant to the calculation of the IBG spectral response, which decreases considerably the computation time. Furthermore, we present an IBG emulator that allows estimation of expected fabrication yield of specific gratings given that the fabrication process is well characterized. The analysis of apodized gratings is used as an example to illustrate how this modeling can help to reduce development cost by first studying robustness of IBG designs to fabrication limitations. Finally, we study analytically the impact of sidewall roughness having short correlation lengths and small roughness variances on the spectral response of weak gratings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".