Low-Loss Passive Si$_{3}$N $_{4}$ Serial-to-WDM Interface for Energy-Efficient Optical Interconnects
Bibliographic record
Abstract
The increasing number of computational servers in data centers is imposing tighter constraints on the networking infrastructure. Scalable power efficient optical interconnect network becomes necessary to leverage the bandwidth capacity of current electronic switches or opto-electronic components. Hence, novel optical interconnect technology can enhance the network capacity by harnessing the feasibility of simultaneous processing of optical signal in the wavelength and time domains. In this paper, we present a four channel optical passive wavelength-striped mapping (PWSM) device, which passively time compresses/expands serial packets through optical wavelength multiplexing/demultiplexing. The PWSM device, which has a 1 × 4 channel optical wavelength demultiplexer with integrated optical delay lines, is designed in a low-loss Si3N4(propagation loss ~3.1 dB/m) waveguide platform. The PWSM device multiplexes/demultiplexes four WDM channels and offsets in time the adjacent channels to optically serialize/deserialize data packets. In this demonstration, a 64 ns long data packet is formed at the output of the device by combining four 16 ns data segments of the packet in time domain. Incremental optical insertion loss between adjacent channels is ~9.7 dB due to the integrated passive optical delay waveguides. The data rate of the four segmented packets and the combined packet is 25 Gb/sec. We have measured a bit error rate performance below 1 ×10-9for the 64 ns serial data packet regenerated by the PWSM device for a received optical power of -6.7 dBm.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".