Experimental Identification of the Failure Modes and Failure Mechanisms of Fiber to Waveguide Couplings Under Cyclic Tensile Loading
Bibliographic record
Abstract
The need for a lifetime prediction methodology for optoelectronic devices has become a necessity as they become more widely used. The development of such a methodology first requires the identification of the most relevant failure modes and mechanisms activated in service. This paper reports on an experimental investigation of the failure modes and failure mechanisms of fiber-to-waveguide coupling (FWC) under tensile cycling loading. Samples with a geometry similar to an industrial silicon photonic chip with V-groove alignment features were fabricated for ribbons of 12 single mode fibers. A cyclic tensile loading with a constant amplitude between 10 and 35 N in the direction of the free end of the ribbon and a frequency of one load cycle per 10 seconds was used. The insertion loss for all 12 fibers was continuously monitored. Three failure modes were observed during tensile cycling reliability tests: slow interference cycles on the optical power (mode I) that accumulated over tensile cycles, fast cycles (mode II) that were in phase with the tensile load cycles, and a sudden power drop due to fiber breakage (mode III). Mode I occurred when a fiber accumulated irreversible displacement under tensile cycles due to slippage in a fully delaminated strain relief (SR). Failure Mode II was observed when the ribbon was only partially detached from the SR and the fiber was disconnected from the V-groove, resulting in reversible displacement cycles under tensile loading. This study contributes to a first step towards the development of a lifetime prediction methodology for optoelectronic devices, by identifying failure modes and mechanisms that can be accelerated and related to service conditions with a lifetime model like the Coffin-Manson model.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".