Single-Etch Silicon Nitride Grating Couplers for Multiband Applications in Quantum and Fiber Communications
Bibliographic record
Abstract
Silicon nitride (Si3N4) is an attractive alternative to the silicon-on-insulator platform due to its broad spectral transparency window, low waveguide losses, and negligible twophoton absorption. However, the moderate refractive index contrast between the Si3N4 waveguide core and the cladding presents challenges, including limiting the efficiency and performance of surface grating fiber-chip coupling devices. Addressing this issue is crucial to fully leveraging the advantages offered by the silicon nitride platform. While various strategies have been developed to enhance the performance of surface grating couplers, they often come with increasingly complex fabrication requirements. In this work, we present a set of highefficiency silicon nitride grating couplers using standard singleetch fabrication processes. The devices are designed for three spectral regions: 950 nm, 1310 nm, and 1550 nm. Uniform grating couplers demonstrated experimental coupling efficiencies between -5.9 dB and -3.1 dB. Record-breaking performance was achieved using subwavelength metamaterial apodization and beam focalization, resulting in fiber-chip coupling losses as low as -2.5 dB, all achieved through a straight-forward single-etch fabrication process.
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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".