Compact inverse-designed tilted waveguide crossing
Bibliographic record
Abstract
Waveguide crossings are key components for increasing integration density and routing flexibility. We propose a novel, to the best of our knowledge, compact, tilted silicon waveguide crossing designed using inverse design methods, specifically optimized to minimize both insertion loss and crosstalk. Through adjoint optimization algorithms and finite-difference time-domain simulations, we achieve a significant reduction in crosstalk from −46 dB at 90° to −54 dB at 86°, with a remarkably low insertion loss of −0.14 dB at 1310 nm. The footprint is further minimized to only 8 × 8 μm2, setting what we believe to be a new benchmark in terms of both size and performance compared to existing silicon waveguide crossings. The impact of the tilt angle on performance is thoroughly analyzed through both simulations and experimental validation. This device highlights the potential of inverse design to deliver optimized overall performance while maintaining an ultra-compact footprint. Fabricated on a commercial foundry, with its low loss, low crosstalk, and small size, our waveguide crossing is ideally suited for high-density photonic integrated circuits, offering promising applications in data centers and quantum photonics.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".