A perturbation approach for the design of coupled resonator optical waveguides (CROWs)
Bibliographic record
Abstract
We propose a simple and accurate technique for the design optimization of coupled resonator optical waveguides (CROWs). The technique is based on tapering the coupling coefficients at the CROWs stages to achieve arbitrary realizable filter response. A perturbation theory is developed for the linearization of the design problem. The coupling coefficients are expanded around a known mean coupling value. By dumping the higher order perturbation terms, we ignore the effect of multiple reflections among the rings introduced by the small adjustment of the coupling coefficients. This is a first order accurate approach as it takes into account only multiple reflections introduced by the zero order terms. The design problem is then formulated as an optimization problem. The optimal filter design is achieved by solving a constrained linear least square problem. This optimization problem can be solved efficiently to get the global optimal design. Our technique is accurate and efficient compared to other nonlinear approaches. Our technique has been verified using different proposed targeted filter responses utilizing third, fifth, and tenth order coupled ring resonators. Highly selective tenth order filter with flat response is proposed. We compared our results to the existing techniques to prove its accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".