Reliability Modelling of Optical Fiber Couplers Based on Accelerated Tensile Cyclic Tests
Bibliographic record
Abstract
This paper introduces a methodology designed to predict the reliability of optoelectronic devices through accelerated testing. To imitate an industrial silicon photonics chip, we fabricated a sample with a similar geometry that incorporated V-groove alignment features for fiber coupling. Two reliability tests, the tensile cycling test (TCT) and the side tensile cycling test (STCT), were performed to accelerate sample failure and generate fatigue failure data. Both tests revealed the same failure mode, which is characterized by the appearance of slow intensity cycles in the insertion loss due to a growing gap between optical fibers and the adhesive at the joint, resulting from fiber displacement during tensile cycles after strain relief (SR) failure. The power law model (PM) was applied to the collected fatigue data for both reliability tests. The PM revealed that both tests shared a common failure mechanism, involving minor delaminations between the ribbon coating and the SR adhesive. Notably, the PM exponents from STCT (0.80 ± 0.02) and low-load TCT (0.812 ± 0.002) suggest a similar mechanism, while high-load TCT showed a distinct mechanism (2.324 ± 0.001), characterized by adhesive residues dragged by the ribbon after breakage due to high loads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".