Packaging of photonic components VII: the process for coupling optical fibers to planar waveguides with thermal curing adhesives
Bibliographic record
Abstract
Our developed thermal curing adhesives were reported having excellent performance in coupling optical fibers to waveguides. The fiber-to-waveguide coupling process based on these adhesives is reported in this paper. The process consisted of three major steps in the process, including loading waveguide dies and fiber arrays onto the sample holders, aligning the fibers to waveguides at a constant temperature to reach minimum loss, bonding the fiber arrays to waveguide dies. The sample holders, which used ceramic spaces to isolate heat and springs to damp stress, were specially designed to keep fiber arrays and waveguide dies at a constant temperature up to 120°C with minimum shift. When the fiber arrays and waveguide dies were equilibrated with the set temperature, a rough alignment was conducted manually, followed by an automatic alignment controlled by a Melles Griot system. Then, the adhesives with proper viscosity and curing rate were applied to the gaps between fiber array and waveguide dies to bond them together. The curing temperature was optimized so that the adhesives could be distributed rapidly and cured at a speed that still allowed a small alignment adjustment during the curing. Such a temperature optimization was achieved by studying the adhesives’ curing kinetics with a DSC.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".