Robust single-etch surface grating couplers for silicon nitride waveguide platform
Bibliographic record
Abstract
Silicon nitride (SiN) has emerged as an important waveguide platform to implement integrated photonic circuits for a wide range of applications, including telecommunications, nonlinear optics, and quantum information. The SiN platform is compatible with a CMOS fabrication and provides attractive properties such as low propagation loss and increased tolerance to fabrication errors. However, a comparatively low index contrast is a challenge for coupling light off-chip using surface grating couplers. Compared to silicon waveguides, reduced grating scattering strength limits attainable coupling efficiency since the size of the radiating beam is significantly larger compared to near-Gaussian optical mode of standard SMF-28 optical fibers. In this work, we present, both theoretically and experimentally, a set of robust uniform and apodized grating couplers implemented in 400 nm SiN platform. Grating couplers operate with TE polarization at telecom C-band, with measured coupling losses between -4 dB to -3 dB near 1550 nm wavelength. Prospectively, our designs can be further optimized by using sub-wavelength grating metamaterial engineering and self-focusing topology, with simulated fiber-chip coupling loss as low as -1.6 dB. Our results pave the way towards development of highly efficient and robust off-chip coupling interfaces in SiN 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) | 0.000 | 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".