A simple and intuitive approach for assessing the grid density and the propagation step for BPM modeling of components
Bibliographic record
Abstract
The beam propagation method (BPM), in both two-dimensional and three-dimensional versions, is a widely used tool for modeling optical building blocks of photonic integrated circuits (PIC) and integrated optics devices. Such optical building blocks include bent waveguides, couplers, splitters, angled waveguides, etc. Most of the time, trial BPM runs need to be executed to properly set the grid density and propagation step in order to obtain stable and repeatable results. Often, these practice runs can consume a large quantity of valuable design time and computational resources, especially when modeling devices that require short propagation steps and a very dense computation grid. We propose a method that helps the BPM user to quickly assess a range of values for the grid density and propagation step, which enables adequate modeling without resorting to numerous BPM runs. This straightforward and highly intuitive method is based on what we have called the Overlap Quantization Error (OQE). It is also independent of the BPM algorithm used for the simulations. To illustrate the technique, several simulation results are presented for both high- and low-contrast curved waveguides.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".