Coupling strategies for integrated photonics: advances and opportunities
Bibliographic record
Abstract
Integrated photonics is envisioned as a key enabler for numerous emerging applications, including high-speed communications, ultra-fast optical computing, and quantum information processing. These applications call upon efficient connection between the sub-micrometric planar photonic waveguides and standard optical fibers (SMF-28). Fiber-to-chip optical interfacing has always been recognized as an issue of fundamental importance in the field of integrated photonics, particularly by imposing a critical constraint on the power budget for the chip-scale photonic systems. Hence the direct fiber-chip connection is vastly inefficient with position-restricted accessibility, the key challenge for light coupling involves several factors. This includes geometrical and material discrepancies, different mode field diameters of SMF-28 fibers and photonic waveguides, on-chip design flexibility, and fast optical testing, preferably utilizing available die-level or wafer-scale accessories. In this work, we report on our recent progress in the development of efficient fiber-to-chip optical interfaces based on compact surface grating couplers. We present and discuss prospective design approaches and experimental results for grating-coupled devices implemented on surging silicon (Si) and silicon nitride (SiN) waveguide platforms, supporting advanced photonic integration with coupling efficiencies approaching -1 dB level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".