Silicon Photonic Phase-Diverse Receiver Enabling Transmission of >Net 250 Gbps/λ Over 40 km for High-Speed and Low-Cost Short-Reach Optical Communications
Bibliographic record
Abstract
The fast growth of data traffic in short-reach optical communications drives the demand for high-speed and low-cost optical modules. Here we demonstrate a silicon photonic (SiP) phase-diverse receiver based on our recently proposed asymmetric self-coherent detection (ASCD) scheme with Mach-Zehnder interferometers (MZI). The SiP MZI-ASCD receiver has a hardware-efficient architecture and recovers complex double-sideband signals via the beatings between a signal portion and a delayed signal portion from an asymmetric MZI using only 2 single-ended photodiodes and 2 analog-to-digital converters. The removal of the local oscillator in the direct detection receiver enables low-cost uncooled lasers, whereas the phase diversity closes the gap with coherent detection in terms of electrical spectral efficiency (ESE). Using a SiP MZI-ASCD receiver with a 15 ps delay, we detect a 60 Gbaud single-polarization 16-QAM signal transmitted over 40 km of single-mode fiber (SMF) below 20% overhead (OH) SD-FEC threshold of 2 × 10−2with a net bit rate of 200 Gbps and a record net ESE of 5.86 b/s/Hz per wavelength per polarization. Using a SiP MZI-ASCD receiver with a different delay of 7.5 ps, transmission of single-polarization 16-QAM DSB signal up to 80 Gbaud over 40 km is achieved below 24% OH SD-FEC threshold of 4.5 × 10−2with a record net bit rate of 258 Gbps and a net ESE of 5.31 b/s/Hz per wavelength per polarization.
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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 | 0.001 |
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".