Application-tailored optimisation of photonic crystal waveguides
Why this work is in the frame
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Bibliographic record
Abstract
Photonic crystal (PhC) waveguides are used for a wide range of applications with diverse performance metrics. A waveguide optimised for one application may not be suitable for others and no one-size-fits-all solution exists. Therefore each application requires a specialised waveguide design, a computationally and time intensive process. Here, we present a hybrid, multi-objective optimisation routine for PhC waveguides, to efficiently guide the device design. The algorithm can be configured to optimise for a wide range of performance metrics and applications. We demonstrate optimisations for three different applications: slow light performance, propagation loss due to fabrication disorder and delay line applications. For each optimisation target, our routine quickly finds practical waveguide designs (<48 h, on a laptop computer) that match or exceed the performance of state-of-the-art devices designed by the community over the last 10 years. This is also the first time that scattering loss from fabrication disorder has been incorporated into an optimisation algorithm, ensuring realistic predictions of a PhC waveguide design’s practical performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it