High-performance silicon Bragg filters exploiting sub-wavelength and symmetry engineering (Conference Presentation)
Bibliographic record
Abstract
Bragg filters stand as a key building blocks of the silicon-on-insulator (SOI) photonics platform, allowing the implementation of advanced on-chip signal manipulation. However, achieving narrowband Bragg filters with large rejection levels is often hindered by fabrication constraints and imperfections. Here, we present a new generation of high-performance Bragg filters that exploit subwavelength and corrugation symmetry engineering to overcome bandwidth-rejection trade-off in state-of-the-art implementations. We experimentally show flexible control over the width and depth of the Bragg resonance, unlocking new tools for the implementation of notch filters with arbitrary bandwidth and rejection level. These results pave the way for the implementation of high-performance on-chip wavelength filters with a great potential for nonlinear-based applications, e.g. next generation Si-based photon-pair sources for quantum photonic circuits.
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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) | 0.005 | 0.002 |
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".