Silicon Photonic Single-Segment IQ Modulator for Net 1 Tbps/λ Transmission Using All-Electronic Equalization
Bibliographic record
Abstract
There is a continuous need to scale optical communication networks' capacity to cope with the exponential growth of data traffic. Silicon photonics (SiP) retains significant potential as a platform for optical transceivers due to its CMOS compatibility, despite its limited electro-optic bandwidth and high driving voltage requirements. Here we present the design and characterization of two single-segment C-band SiP in-phase quadrature modulators (IQM) that differ in the phase shifter length, and we analyze the design tradeoffs based on their transmission performance.The large-signal transmission experiments indicate that the long IQM supports higher data transmission rates, which has 36 GHz 6-dB bandwidth and 10.5 V DC Vπunder 1 V reverse bias.With all-electronic equalization and on a single polarization, we transmit net 413 Gbps (95 Gbaud 32QAM) over 80 km of standard single-mode fiber (SSMF) under the 14.8% overhead concatenated forward error correction (C-FEC) BER threshold of 1.25 × 10−2. Using dual-polarization (DP) emulation and lookup table-based non-linear pre-distortion (NLPD), we demonstrate the transmission of 95 Gbaud DP-32QAM and 115 Gbaud DP-16QAM over 80 km of SSMF below the C-FEC BER threshold, corresponding to net rates of 827 Gbps and 800 Gbps, respectively. Moreover, we transmit 105 Gbaud DP-64QAM over 80 km below the 25% overhead soft-decision (SD) FEC BER threshold of 5 × 10−2; featuring the first demonstration of net 1 Tbps transmission using an all-silicon IQM. Employing only electronic equalization and single-segment IQM preserves the conventional architecture of coherent networks and transceivers, and highlights the potential of SiP as a platform for next-generation 800G applications.
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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".