A Dual-Polarization Silicon-Photonic Coherent Transmitter Supporting 552 Gb/s/wavelength
Bibliographic record
Abstract
Coherent optical links improve spectral efficiency over their intensity-modulation direct-detect (IM-DD) counterparts using advanced modulation schemes such as quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM), and by utilizing dual polarization (DP) of light. This increase in spectral efficiency also leads to stringent requirements for the link components. The transmitter (TX) must simultaneously achieve high bandwidth (BW), linearity, output swing, and reliability. In this article, we present a silicon-photonic Mach-Zehnder modulator-based optical TX on an Silicon-on-Insulator (SOI) process, and linear, high-swing SiGe drivers on 130-nm BiCMOS process. The output stage of the driver uses a voltage breakdown enhancement technique to ensure the reliability of the TX. A resistor-based capacitor splitting technique is introduced, and aided by other methods such as zero-peaking and degeneration, the targeted gain, BW, swing, and linearity for the driver are realized. The driver achieves a differential swing of 6 V peak-to-peak, a total harmonic distortion (THD) of 3.6%, and more than 40 GHz of electrical BW. Co-designed and co-packaged with the silicon-photonic modulators, the TX achieves 272 Gb/s/wavelength with DP-16 QAM at 6-V peak-to-peak driver swing and exceeds 0.5 Tb/s/wavelength data rates with DP-16 QAM at 2.4-V peak-to-peak driver swing. The low-cost, compact, and all-Si/SiGe design matches the required optical SNR performance of LiNbO3 modulators with III-V drivers at 34 Gbaud.
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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".