Inverse-designed silicon nitride reflectors
Bibliographic record
Abstract
Reflectors play a pivotal role in silicon photonics since they are used in a wide range of applications, including attenuators, filters, and lasers. This Letter presents six silicon nitride reflectors implemented using the inverse design technique. They vary in footprint, ranging from 4 µm × 3 µm to 4 µm × 8 µm. The smaller device has an average simulated reflectivity of -1.5 dB, whereas the larger one exhibits an average reflectivity of -0.09 dB within the 1530 to 1625 nm range. The latter also presents a 1-dB bandwidth of 172 nm, spanning from 1508 to 1680 nm. Despite their resemblance to circular gratings, these devices are more intricate and compact, particularly due to their non-intuitive features near the input waveguide, which include rough holes and teeth. The roughness of these features significantly contributes to the performance of the devices. The reflectors were fabricated on a silicon nitride multi-project wafer (MPW) through a streamlined process involving only a single etching step. The 4 µm × 8 µm reflector demonstrates a remarkably high reflectivity of -0.26±0.11 dB across the 1530 to 1600 nm range, rendering it suitable for high-quality factor cavities with direct applications in lasers and optical communications.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".