Scalable SiPh-InP Hybrid Switch Based on Low-Loss Building Blocks for Lossless Operation
Bibliographic record
Abstract
We design and experimentally demonstrate scalable 2 × 2, 4 × 4 and 8 × 8 silicon photonic (SiPh) thermo-optic switch exhibiting low loss, low crosstalk, low power penalty, and BER below 10-10for payload data transmission. Less than 3.13 dB insertion loss (IL) and approximately 20.5 dB crosstalk is measured in the 8 × 8 SiPh banyan switch with thermal phase shifters. We also report on a semiconductor optical amplifier (SOA) in an indium phosphide (InP) technology platform with 25 dB gain and 7 dB noise figure enabling to transmit optical signals with large OSNR. Combining SiPh and InP technologies, we propose a lossless hybrid switch matrix with distributed SOA-based gain capable of transmitting data with near zero loss and low crosstalk over a large switching matrix. In hybrid SiPh/InP switches, the SOA gain compensates for the SiPh switch loss at the cost of amplified spontaneous emission (ASE) noise but mitigated by bandpass optical filters. Lower IL from the SiPh switch requires less gain from the SOAs leading to less OSNR degradation. Experimentally validated building blocks confirmed scalability up to 64 × 64 in SiPh-InP hybrid platform.
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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".