Integrated silicon nitride devices via inverse design
Bibliographic record
Abstract
Integrated photonic devices made of silicon nitride, which can be integrated with silicon-on-insulator and III-V platforms, are expected to drive the expansion of silicon photonics technology. However, the relatively low refractive index contrast of silicon nitride is often considered a limitation for creating compact and efficient devices. Here, we present three freeform silicon nitride devices-a coarse wavelength-division multiplexer, a five-mode mode division multiplexer, and a polarization beam splitter-while systematically benchmarking both the design capability and the fabrication repeatability and robustness of inverse-designed components. We demonstrate up to a 1200× reduction in footprint while maintaining relatively large minimum feature sizes of up to 160 nm, showing that inverse-designed silicon nitride devices can be as compact as their silicon counterparts. These results enable high-density integration in silicon nitride photonics and pave the way for multidimensional data transmission and quantum applications, as the inverse design technique can be applied to different silicon nitride thicknesses and is potentially extendable to other low- and mid-index contrast platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".