Reconfigurable and Scalable Multimode Silicon Photonics Switch for Energy-Efficient Mode-Division-Multiplexing Systems
Bibliographic record
Abstract
A novel integrated scalable multimode switch (SMS) is experimentally demonstrated using tapered multimodeinterference-based couplers and Ti/W metal heater phase shifters for mode-division-multiplexing (MDM) silicon photonics switching. The SMS allows path-reconfigurable switching of the first two (TE0 and TE1) or the first three (TE0, TE1, and TE2) transverse electric (TE) modes using the same device achieving footprint efficiency for higher bandwidth density. A proof-of-concept realization of the two-mode switch demonstrates the (de)multiplexing and switching of broadband optical signals over the TE0 and TE1 modes exhibiting -6.5 dB insertion loss (IL) in the bar state and -7.3 dB IL in the cross state at 1550 nm with less than -14dB crosstalk. Simultaneous switching of two parallel TE modes (TE0+TE1) exhibits less than -7.0 dB IL and -11.9 dB crosstalk at 1550 nm. An aggregated bandwidth of 2 X 10 Gb/s is experimentally achieved while switching between two non-return-to-zero PRBS31 data signals with17.7 dB switching extinction ratio (ER) for individual-mode transmission, and12.0 dB switching ER for dualmode transmission. The SMS is scalable to switch higher order TE modes with lower energy consumption (up to 63% less) than the single-mode switches indicating its potential application in energyefficient MDM photonic networks.
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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".