Study of waveguide crosstalk in silicon photonics integrated circuits
Bibliographic record
Abstract
Silicon photonics is going trough a terrific expansion driven by several applications, from chip wiring to integrated sensors and telecommunications. Some applications, e.g. intra and inter chip connections and sensing, require long parallel waveguides for wiring or for connecting grating couplers (GCs) to devices situated in sensing micro-channels. In well packed photonics chips there are often long wiring waveguides parallel for several mm, so loss can be caused by light coupled back and forth between them (cross-talk), by scattering, wall roughness, mode mismatch, etc. This work aims to investigate cross-talk for long parallel waveguides, and to propose methods to reduce cross-talk loss when high integration density is required. We have designed and fabricated about 200 testing structures exploiting e-beam on silicon on insulator (SOI) chip, in order to test several parameters and to find out dominant loss mechanisms. All devices have been tested and measured using an automatic optical bench, in the wavelength range between 1500-1600 nm. Achieved results are promising, since they allow for comparing cross-talk for short as well as long interaction lengths (up to 5 mm), different waveguide width pairs, several separation distances, and for TE and TM polarization. For smaller gaps, having not symmetric pair of waveguides is very beneficial, since it results in a lower power coupling, e.g. about 20/14 dB of crosstalk reduction for TE/TM waveguides after 5 mm of propagation and gap of 0.5 μm. This can be very useful for the design of integrated photonics chips requiring high-density packaging of devices and waveguides.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".