Thermally chirped contra-directional couplers for residueless, bandwidth-tunable Bragg filters with fabrication error compensation
Bibliographic record
Abstract
Bandwidth-tunable filters are essential in elastic optical networks for dynamic bandwidth allocation. Existing solutions in silicon photonics face challenges to meet requirements in real-world applications due to design trade-offs and fabrication errors. In this Letter, we propose and experimentally demonstrate a silicon photonic tunable add-drop filter in a single-stage, hyperbolic-tangent-apodized contra-directional coupler with a segmented microheater. It allows to create an arbitrary temperature profile along the device for bandwidth tuning in both through and drop responses. We show that the algorithmic operation of the device can effectively compensate local fabrication nonuniformity and improve the out-of-band suppression ratio by 69%. Applying proper temperature offsets and slopes allows to continuously tune the filter's center wavelength over 8 nm and its drop-port 3 dB bandwidth between 14.0 and 22.4 nm.
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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".