O-band-operated fiber-chip surface gratings for silicon nitride photonic integrated circuits
Bibliographic record
Abstract
Advanced optical interconnects call upon innovative approaches to establish efficient photonic chip interfaces. Surface grating couplers are prime candidates for low-loss coupling between conventional optical fibers and photonic integrated circuits (PICs). Persistently, this remains a challenging task due to material, modal, and geometrical disparities. In this work, we report on recent advances in the design and fabrication of subwavelength grating (SWG) metamaterial-engineered silicon nitride (SiN) grating-coupled optical interfaces. The unique properties of SWG metamaterials offer an extra degree of freedom for controlling light in grating couplers, thus improving both diffraction and fiber-coupler field matching efficiencies. In turn, this enhances fiber-chip coupling performance, while maintaining cost-effective and foundry-compatible fabrication. Our SiN couplers, manufactured in open-access photonic prototyping facilities, have coupling losses as low as -3.1dB at the 1310nm wavelength. Moreover, hybrid SiN grating couplers with amorphous silicon (α-Si) overlays and SWG metamaterials achieve further reduced coupling losses of -2.4dB and -1.3dB for uniform and apodized layouts, respectively. These results underscore the potential of metamaterial-engineered couplers for low-loss fiber-chip interface integration within scalable SiN PICs for foundry-compatible optical and quantum systems.
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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".