Numerical analysis of a novel straight polymer channel waveguide based variable optical attenuator
Bibliographic record
Abstract
Variable optical attenuators (VOAs) play an important role in the wavelength division multiplexed (WDM) telecommunication networks. It is often necessary to use VOAs to perform functions as: (a) Dynamic channel balancing at MUX location; (b) Dynamic channel leveling at add/drop sites; (c) Receiver overload control; and (d) Optical channel blocking. In order to minimize the overall costs and optimize the performances, VOAs are sometimes required to be integrated with other optical components, such as a MUX/DEMUX, a detector array and etc. Planar lightwave circuit (PLC) technology is the ideal technology platform for realizing this type of large-scale integrations. In this work, we propose a VOA design based on a simple straight polymer channel waveguide layout in order to provide the optimal optical performances with very low electric power consumptions and high fabrication yield. The fabrication of the proposed design will be in compatible with other polymer components on the same substrate. It is found from the simulation results that the design can offer high attenuation level with very low electrical power consumption. In addition, the simulation results demonstrate that the proposed VOA design can achieve very low polarization dependent loss and very good spectra flatness. All these performances would make the proposed VOA design very suitable for large-scale integration applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".