Low-loss polymeric materials for passive waveguide components in fiber optical telecommunication
Bibliographic record
Abstract
With fiber optical telecommunication systems penetrating into metropolitan and access networks, planar waveguide technology is increasingly being considered a solution to the bottleneck of cost-effective manufacture of passive components. Being recognized for their high thermo-optical coefficients, ease of fabrication, cost-effectiveness, and compatibility with other materials, polymers as a platform technology for waveguide devices are gaining more and more commercial acceptance. Fully exploiting the potentials of the polymeric materials demands comprehensive understanding of both the specific device applications and various polymer systems. The right choice of materials is often the key to the success of component development. Unfortunately, since extensive study of polymeric materials and devices operating at 1.55 μm began just recently, few ideal materials have so far been made commercially available. From the polymer chemistry point of view, it is possible to tailor the materials to meet specific and strict requirements for optical waveguide devices. This is a review of the most promising fluorinated polymers and silicone resins and their demonstrated device applications. The paper is designed to provide a guide to both polymer scientists who want to develop novel high-performance materials for waveguide applications, and optical engineers who need to gain insight into the materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".