Silicon-based surface gratings for efficient fiber-chip and free-space beam coupling
Bibliographic record
Abstract
Silicon photonics has established itself as a key integration platform, leveraging high-quality materials and large-scale manufacturing using mastered toolsets of complementary metal-oxide-semiconductor (CMOS) foundries. Chip-scale photonics offer unique promises for dense integration of versatile optical functions through compact and high-performance building blocks. Integrated photonics is now competing technology for many applications, spanning from telecom/datacom and interconnects up to quantum sciences and light detection and ranging (LIDAR) systems, among others. However, the lack of low-loss input/output chip interfaces can be prohibitive to successfully deploy multi-diverse device applications. Low coupling loss is essential in reducing overall power budget in photonic systems, impacting on-chip integration level. The light coupling from an off-chip environment into the planar waveguide platforms has always been a challenging research problem since the early years of integrated photonics. Optical interfaces formed on a photonic chip surface, rather than implemented on a chip edge, have been widely used to access photonic circuits with optical fibers or enabling free-space coupling of light beams. Surface gratings can be positioned at arbitrary locations and/or arranged in pre-defined patterns on the chip, facilitating wafer-scale testing and optical packaging. In this work, we present our recent progress in the development of silicon-based surface gratings for use in fiber-to-chip and free-space beam coupling. In particular, we discuss prospective design approaches to develop low-loss surface grating couplers implemented on silicon-on-insulator (SOI), silicon nitride (SiN), and hybrid silicon-silicon nitride (Si-SiN) platforms, allowing to approach a coupling loss below -1 dB. Among these, we also cover contemporary advances in compact silicon metamaterial nano-antennas for dense optical phased arrays, obtaining high a diffraction performance (> 90%) and wideband operation (> 200 nm) simultaneously.
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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 | 0.001 |
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".