Metamaterial-engineered fiber-chip couplers in silicon and silicon nitride waveguides
Bibliographic record
Abstract
Integrated photonics has become a mainstream technology driven by advances in optical communications and leveraging mature processing infrastructure of silicon microelectronics. In practical applications of photonic integrated circuits (PICs), the presence of low-loss off-chip optical coupling interfaces is of key importance. Photonic chips require optical connection to the external world, facilitating both multi-channel fiber connections and free-space multi-port optics. Optical coupling in and out of the planar waveguide circuits is still a critical challenge in integrated photonics due to limitations caused by geometrical, material, and modal mismatches. The utilization of subwavelength grating (SWG) metamaterials, i.e. nano-structured waveguide segments with structural periodicity smaller than the wavelength of the propagating light, is often harnessed as an effective design tool to improve the performance of fiber-chip optical couplers, without compromising the fabrication simplicity. In this work, we present recent advances in the development of low-loss photonic chip interfaces based on surface grating couplers with SWG metamaterials. In particular, we report on advanced design solutions of surface gratings realized on silicon and silicon nitride waveguide platforms, facilitating effective control of polarization and enhanced fiber-chip coupling performance with losses down to −1dB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".