Inverse design fiber-to-chip couplers for the O- and C-bands
Bibliographic record
Abstract
High-efficiency fiber-to-chip couplers are essential for high-performance optical interconnects. In this Letter, we experimentally demonstrate two inverse-designed silicon-on-insulator (SOI) couplers tailored for single-mode fibers (SMFs) in the C and O telecommunication bands. The O-band coupler represents the first, to the best of our knowledge, experimental demonstration of a topology-optimized coupler for this band while maintaining a minimum feature size of 120 nm. Both couplers operate at an 8° angle and are optimized for TE polarization. The C-band coupler achieves a coupling efficiency of -3.3 dB with a 3-dB bandwidth of 64 nm, while the O-band coupler reaches -3.4-dB efficiency over a 3-dB bandwidth spanning from 1292 nm to 1355 nm. Measuring 12 μm by 12 μm, these devices are designed using a single optimized silicon layer, reducing fabrication complexity and achieving efficiencies comparable to those of much larger high-performance grating couplers. Their compact size can increase integration density and contribute to reducing fabrication costs. Additionally, these couplers could be suitable for spatial division multiplexing (SDM) interconnects using multicore fibers, where the mode field diameter is compatible with single-mode fibers. They could also be used with multimode fiber configurations, where multiple couplers could be combined to generate higher-order modes.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".