Inverse-Designed 90-Degree Silicon Nitride Bends for the C Band
Bibliographic record
Abstract
We demonstrate the use of inverse design to achieve high-efficiency, compact$90^{\circ }$bends with radii as small as$6 \,{\mu }\mathrm{m}$with silicon nitride waveguides. These bends are designed for the TE polarization of single-mode waveguides with a cross-section of$850 \,{\mathrm{nm}}$×$400 \,{\mathrm{nm}}$, optimized for the C-band. Both simulations and experimental results confirm that the freeform bends obtained through inverse design outperform conventional circular and Euler bends across four footprint sizes ranging from$6$×$6 \,{\mu }\mathrm{m}^{2}$to$21$×$21 \,{{\mu }\mathrm{m}}^{2}$. All inverse-designed bends were fabricated using an e-beam lithography process and designed with a minimum feature size of 160 nm, ensuring maximum compatibility with commercial optical lithography processes and viability for large-scale production. Simulations for the$6 \,{\mu }\mathrm{m}$radius bends show that the best freeform bend exhibits an average loss of$1.62 \,{\mathrm{dB}}$across the entire C-band, compared to$2.88 \,{\mathrm{dB}}$for circular bends and$3.46 \,{\mathrm{dB}}$for Euler bends. Experimental results align well with simulations, with measured losses of$1.89 \,{\mathrm{dB}}$,$2.86 \,{\mathrm{dB}}$, and$3.44 \,{\mathrm{dB}}$for the freeform, circular, and Euler bends, respectively. For the$11 \,{\mu }\mathrm{m}$bending radius, one of the freeform structures demonstrated low losses of$0.18 \,{\mathrm{dB}}$, representing reductions by factors of up to$6.6$and$1.8$times compared to Euler and circular bends, respectively. For the$16 \,{\mu }\mathrm{m}$and$21 \,{\mu }\mathrm{m}$radii, the best freeform bends achieved average losses of$0.08 \,{\mathrm{dB}}$and$0.11 \,{\mathrm{dB}}$per$90^{\circ }$bend across the$1530 \,{\mathrm{nm}}$to$1565 \,{\mathrm{nm}}$wavelength range. These results highlight the potential of inverse design to enable significantly more compact routing in silicon nitride photonic chips, facilitating the development of high-density photonic circuits.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".