Analysis of Bragg gratings for long-range surface plasmon polaritons using the bidirectional beam propagation method based on scattering operators
Bibliographic record
Abstract
For realization of highly integrated optical circuits, various metallic nanostructures supporting the propagation of surface plasmon polaritons have been extensively studied experimentally and theoretically in recent years. This paper reports on the development of a numerically stable and accurate finite-difference-based bidirectional beam propagation method (FD-BiBPM) for analyzing piecewise z-invariant plasmonic structures. Our method is developed based on the scattering operators. The adoption of complex coefficient rational approximations to the square root operator allows to correctly model the propagation of evanescent modes excited at discontinuity interfaces. In view of the large index contrast at metal-dielectric interfaces, a fourth-order accurate finite difference formulation for discretization is incorporated to the present method and its fine treatment of these interfaces guarantees accuracy. By using the present method, the reflection and transmission spectra of the Bragg gratings consisting of a thin metal film embedded in dielectric medium and an array of equidistant metal ridges on each side of the film are calculated. The good agreement of our results with the previously reported simulations illustrates the potential of the newly developed FD-BiBPM for the analysis of longrange surface plasmon polariton (LRSPP) waves guided along the described Bragg gratings.
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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) | 0.000 | 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) | 0.001 | 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".