Nanophotonic Bragg grating assisted Mach–Zehnder interferometers for O-band add-drop filters
Bibliographic record
Abstract
Spectral filters are fundamental building blocks in integrated photonics. Bragg grating filters have been demonstrated in silicon waveguides with a wide range of spectral responses and are suitable for wavelength division multiplexing applications. However, retrieving Bragg grating reflections typically requires external components such as fiber optic circulators. In this work, we develop fully integrated add-drop filters based on cladding-modulated Bragg gratings incorporated in a Mach-Zehnder interferometer configuration. We design complex spectral filtering devices with single and dual-band flat-top responses for the specified bandwidth. Additionally, we propose a novel design methodology which aims to minimize phase errors within the filters. We experimentally demonstrate add-drop filters with single-band and two-band rejection spectra at the datacom O-band, fabricated on a 220-nm thick silicon-on-insulator platform. Our results show an insertion loss below 1 dB and a crosstalk of around -20 dB at the channel center for a 4.5-nm wavelength grid and 3-nm wide channels.
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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 | 0.001 | 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".