Packaging of photonic components VII: a method to eliminate the bulking and stressing of fiber ribbons in the planar-waveguide-based components
Bibliographic record
Abstract
The bulking or stressing of fiber ribbons in the packaged waveguide-based components is associated with the performance deterioration of the components. The current industrial practice of avoiding the problem is to keep fiber ribbons movable regarding the packaging house. This approach, however, makes the component vulnerable to external load during component handling and does not complied with Telcordia test standard. A special technology based on incorporating a soft gasket was developed in our laboratory to solve the problem. The gasket is made of low-modulus elastomer foam with certain thickness and is positioned between the packaging house and strain relief boots, on which fiber ribbons are bonded with an in-house developed epoxy adhesive that has passed Telcordia test. In the packaged components, any effect caused by the mismatched coefficient of thermal expansion between the packaging house and fiber ribbons are compensated by the gasket, and no bulking or stressing occurs in the fiber ribbons. Meanwhile, since the fiber ribbons are firmly bonded to the strain relief boots, any external force applied on the fiber ribbons is transferred to the packaging house, instead of the fiber arrays and waveguide dies. The packaged component with this technology meets the Telcordia test standard and is cost-effective.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".