Numerical optimization of the fiber Bragg gratings with fabrication constraints
Bibliographic record
Abstract
We propose the iterative layer-peeling algorithm (LPA) and the genetic algorithm (GA) to numerically optimize the fiber Bragg grating (FBG) design. Fabrication constraints are introduced in the design, such that the designed FBGs satisfy the spectral specifications and are easy to fabricate. The powerful numerical optimization permits removing the phase shifts in the FBGs, so that the gratings can be fabricated with conventional high quality holographic phase masks, without need for custom made phase masks. We introduce the inverse LPA, which is faster than the conventional transfer matrix method by two orders of magnitude for analysis of the FBGs. The synthesis of the FBG using the LPA and the analysis of the FBG using the inverse LPA can be iterated in a loop, which allows applying the constraints in both grating and spectrum spaces. The impact of the applied constraints and the convergence are analyzed. The GA is powerful for achieving the global optimum with a higher probability than that of the iterative algorithm and simulated annealing. Our GA is enhanced by a new Fourier series-based real-valued encoding to provide high degrees of freedom, and a rank-based fitness function. The new GA enables us to remove phase shifts in the gratings. All the designed WDM band-pass gratings with minimum dispersion are fabricated using the holographic phase mask without phase shifts. The experimental gratings with dispersion of ± 33 ps/nm in the 0.33 nm flat-top passband will be shown.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".