Mode insensitive switch for on-chip interconnect mode division multiplexing systems
Bibliographic record
Abstract
A mode insensitive switch is proposed and experimentally demonstrated on a silicon-on-insulator platform using a balanced Mach-Zehnder interferometer structure with a mode insensitive phase shifter for on-chip mode division multiplexing interconnects. Switching the first three quasi-transverse electric (TE) modes, consuming less than 40 mW power is demonstrated. The whole system exhibits approximately $ - {2},\;{ - 3.7}$-2,-3.7, and $ - {5.2}\;{\rm dB}$-5.2dB insertion loss for the TE0, TE1, and TE2 modes at 1550 nm, respectively. The corresponding crosstalk is less than $ - {8.6}\;({\rm - 9}), {- 8} ({ - 10.3})$-8.6(-9),-8(-10.3), and $ - {10}\;{\rm dB}$-10dB ($ - {10.3}\;{\rm dB}$-10.3dB) within the wavelength range of 40 nm (1535-1575 nm) for the cross (bar) states, respectively. The extinction ratios (ERs) for the cross (bar) states are 20.1 (19.5), 22.8 (33.7), and 15.4 dB (18.1 dB) for the TE0, TE1, and TE2 modes at 1550 nm, respectively. The payload transmission is also conducted using non-return-to-zero pseudorandom binary sequence (PRBS)-31 data signals at 10 Gb/s for single-mode transmission and simultaneous three-mode transmissions. For all the scenarios, open eyes are observed.
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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) | 0.002 | 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".