Complex spectral filters in silicon waveguides based on cladding-modulated Bragg gratings
Bibliographic record
Abstract
Spectral filters are important building blocks for many applications in integrated photonics, including datacom and telecom, optical signal processing and astrophotonics. Sidewall-corrugated waveguide grating is typically the preferred option to implement spectral filters in integrated photonic devices. However, in the high-index contrast silicon-on-insulator (SOI) platform, designs with corrugation sizes of only a few tens of nanometers are often required, which hinders their fabrication. In this work, we propose a novel geometry to design complex Bragg filters with an arbitrary spectral response in silicon waveguides with laterally coupled Bragg loading segments. The waveguide core is designed to operate with a delocalized mode field, which helps reduce sensitivity to fabrication errors and increase accuracy on synthesized coupling coefficients and the corresponding spectral shape control. We present an efficient design strategy, based on the layer-peeling and layer-adding algorithms, that allows to readily synthesize an arbitrary target spectrum for our cladding-modulated Bragg gratings. The proposed filter concept and design methodology are validated by designing and experimentally demonstrating a complex spectral filter in an SOI platform, with 20 non-uniformly spaced spectral notches with a 3-dB linewidth as small as 210 pm.
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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 | 0.000 | 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".