Ultracompact Silicon-On-Insulator Couplers for Multicore Fibers
Bibliographic record
Abstract
Fiber-to-chip couplers are critical devices to support interconnections between fibers and photonic integrated circuits. The advent of spatial division multiplexing (SDM) systems based on multicore fibers makes these devices subject to increasingly demanding footprint and coupling requirements. In addition to size and efficiency requirements, the manufacturing constraints and large parameter space result in a challenging optimization problem. This article applies topology optimization to design three integrated couplers for multicore fibers with an intercore spacing of 32 μm. By individually optimizing the radiating and tapering regions, we design and experimentally demonstrate two devices: the first with perpendicular coupling and an efficiency of −3.8 dB, with a footprint of 15 μm × 10 μm, and the second with a 10° coupling angle and an efficiency of −2.9 dB, with a footprint of 20 μm × 10 μm. Furthermore, by applying topology optimization over the whole design region, we improved the simulated efficiency to −1.9 dB within a footprint of only 10 μm × 10 μm, which represent the most compact CMOS-compatible coupler to date with efficiency among the highest in class. These are the first devices that can enable direct coupling between silicon chips and multicore fibers with intercore separation below 25 μm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".