On‐chip scalable mode‐selective converter based on asymmetrical micro‐racetrack resonators
Bibliographic record
Abstract
Abstract Mode division multiplexing (MDM) technology has been well known to researchers for its ability to increase the link capacity of photonic network. While various mode processing devices were demonstrated in recent years, the reconfigurability of multi‐mode processing devices, which is vital for large‐scale multi‐functional networks, is rarely developed. In this paper, we first propose and experimentally demonstrate a scalable mode‐selective converter using asymmetrical micro‐racetrack resonators (MRRs) for optical network‐on‐chip. The proposed device, composed of cascaded MRRs, is able to convert the input monochromatic light to an arbitrary supported mode in the output waveguide as required. Thermo‐optical effect of silicon waveguides is adopted to tune the working states of the device. To test the utility, a device for proof‐of‐concept is fabricated and experimentally demonstrated based on silicon‐on‐insulator substrate. The measured spectra of the device show that the extinction ratios of MRRs are larger than 18 dB, and modal crosstalk for selected modes are all less than −16.5 dB. The switching time of the fabricated device is in the level of about 40 μs. The proposed device is believed to have potential applications in multi‐functional and intelligent network‐on‐chip, especially in reconfigurable MDM networks.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".