Enabling ROADM in mode division multiplexing networks with mode-selective switches
Bibliographic record
Abstract
Mode division multiplexing (MDM) enhances optical communication systems by introducing an additional multiplexing dimension. We designed and validated a reconfigurable optical add/drop multiplexer (ROADM) prototype using MDM as an optical subsystem targeting data center interconnects and metro optical networks. Our MDM-ROADM employs a mode-selective switch at each node, selectively de/multiplexing the first three transverse-electric (TE) modes. Key components are based on 220 nm silicon photonics, using subwavelength grating structures and inverse design methodology. Machine learning-based fabrication correction, via our tool PreFab, improved mode selectivity by 57% for mode-selective thermo-optic phase shifters. We also developed mode filters for two modes with a crosstalk of −9dB for TE 0 and −15dB for TE 1 within a 35 nm wavelength range. Experimental validation using TE 0 and TE 1 modes at 1555 nm shows an aggregate payload transmission of 80 Gb/s NRZ and a PAM-4 transmission at 40 Gbaud with a bit error rate of 1.1×10 −9 and 3.8×10 −3 , respectively.
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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) | 0.001 | 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".