Fabrication, characterisation, and optimisation of optical fibre lenses and efficient wave-guide coupling using an iterative approach
Bibliographic record
Abstract
The coupling efficiency of a pigtailed optical fiber lens is often a critical parameter in the manufacture of optical devices. For example, efficient coupling from high power semiconductor lasers to optical fiber pigtails with fiber lenses is of great importance as the uncoupled power can affect the lifetime of the device through thermal degradation of the pigtail or its welding point; for optical amplifiers, the coupling efficiency determines the overall gain. Generally, mass-produced optical fiber lenses do not have a consistent enough quality and it is often necessary to discard lenses that exhibit poor coupling. We previously demonstrated a method for digitally modifying optical fiber lenses. We have now developed a scheme whereby we are able to produce high quality optical fibre lenses with a given lens radius and then to iteratively alter it to achieve the required divergence and spot size in a repeatable fashion for efficient coupling to a variety of devices such as semiconductor optical amplifiers, lasers and wave-guides. We have consistently demonstrated 75% coupling efficiency to angled facet semiconductor optical amplifiers at 1550nm with only three-axis adjustment, and believe the true coupling efficiency to be close to 90%. Our powerful scheme allows the use of nearly all of the lenses for a given application.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".