Spectrally Efficient Integrated Silicon Photonic Phase‐Diverse DD Receiver with Near‐Ideal Phase Response for C‐Band DWDM Transmission
Bibliographic record
Abstract
Abstract In this paper, a spectrally efficient silicon photonics phase‐diverse direct‐detection receiver based on asymmetric self‐coherent detection is proposed. The receiver is hardware efficient and composed of only two photodiodes and two analog‐to‐digital channels to accurately recover complex double‐sideband signals by leveraging an optimized integrated silicon racetrack resonator with near‐ideal phase responses. Moreover, the periodicity of the resonator response enables the receiver to support multi‐channel transmission such as dense wavelength‐division multiplexing. The receiver circuit is fabricated and characterized. In 40‐km transmission experiments, a record net electrical spectral efficiency of 7.10 bit s −1 Hz −1 per wavelength and per polarization is achieved, where a net 208‐Gb s −1 32QAM transmission is demonstrated using 29.3‐GHz electrical bandwidth. Furthermore, the receiver achieves net 220‐Gb s −1 16QAM transmissions in 14 channels from 1527 to 1550 nm with 205‐GHz spacing, and a maximum 240‐Gb s −1 net capacity at a single channel. Such a performance is reached with only standard coherent digital signal processing stacks including all‐linear equalization.
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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 | 0.001 | 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".