Frequency Comb Generation Using a CMOS Compatible SiP DD-MZM for Flexible Networks
Bibliographic record
Abstract
On-chip frequency comb generation is a promising solution for seeding a chip-scale optical transmitter for both Nyquist wavelength-division multiplexing (WDM) and orthogonal frequency-division multiplexing. We demonstrate flexible frequency comb generation using a silicon photonic dual-drive Mach-Zehnder modulator fabricated on a CMOS-compatible process. Our on-chip comb has five lines spaced at 20 GHz with a high tone-to-noise ratio of about 40 dB after one stage optical amplification. Our back-to-back transmission achieves bit error rates (BERs) well below 2e-2, the threshold for 20% overhead forward error correction (FEC), for 800 Gb/s using 16-GBd 32QAM on five WDM channels. We also test a seamless 800-Gb/s super-channel using 5×20 GBd 16QAM, with BER below the 7% overhead FEC threshold of 3.8e-3. To the best of our knowledge, this is the first demonstration of high-spectral-efficiency data carried by an all-silicon optical frequency comb. This establishes that a silicon optical frequency comb has sufficient optical signal-to-noise ratio for high-order QAM, as well as excellent stability for super-channels without guard bands, paving the way to an integrated high-spectral-efficiency multi-carrier optical transmitter.
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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) | 0.002 | 0.001 |
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".