Scalability of Silicon Photonic WDM Receivers for Low-Power Optical I/O
Bibliographic record
Abstract
Optical input/output (I/O) must be carefully designed to scale up bandwidth of intra-data center electrical switches, while reducing energy consumption and cost. Innovative receiver architectures are key to achieving these goals. Both cascaded microring resonators (MRRs) and arrayed waveguide gratings (AWGs) can handle multiple wavelengths. For a fixed number of channels, wavelength de-interleavers can lower loss and crosstalk, and also yield more compact solutions. We present a comprehensive model to analyze six receiver architectures; we consider MRR vs. AWG, 1-stage or 2-stage de-interleaver vs. no de-interleaver. We target a 6.4 Tbps per-fiber bit rate and optimize receiver energy efficiency. We sweep combinations of data rate, channel count, and channel spacing. For each receiver under test simulation, we quantify energy consumption per bit and bandwidth density (compactness). Assuming non-return-to-zero (NRZ) modulation, we find that MRR-based receivers can achieve the 6.4 Tbps per fiber target with energy consumption at (or close to) sub-pJ/bit, while AWG-based receivers can only achieve up to 3.2 Tbps per fiber. The MRR-based receivers are generally more energy efficient and have a higher bandwidth density. Area bandwidth density up to 2.9 Tbps/$\text{mm}^{2}$can be achieved by AWGs, and shoreline bandwidth density up to 21 Tbps/mm can be realized by MRRs. Finally, we review promising ways to further enhance the per-fiber bit rate and lower the energy consumption per bit.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".