Yb-doped LMA triple-clad fiber laser
Bibliographic record
Abstract
The ytterbium-doped large mode area triple-clad fiber design allows for a high concentration of ytterbium in the fiber core which is difficult to achieve with a standard double-clad design. The novelty of the triple-clad fiber design consists in adding to the double-clad fiber design, a first clad next to its core. This first clad offers a better control of the core effective area. With this design a low numerical aperture is achievable (~0.06) for highly rare earth doped large mode area fiber. A 33-μm core ytterbium doped fiber has been fabricated using MCVD and solution doping processes. Selective doping and optimized first clad thickness have been used in the triple-clad design to obtain a nearly bending insensitive and nearly diffraction-limited fiber output. The fiber has been tested in a free-running laser configuration and its slope efficiency is 84% with a laser threshold of 1.4 W. A maximum output power of 26 W at 1070 nm has been achieved for a launched pump power of 34 W at 976 nm. The mode-field diameter has been measured at 18 μm and the output beam M<sup>2</sup> quality factor is below 1.1. Both output power and beam quality were not significantly affected by fiber bending with loops diameter as small as 2.5 cm. The optical performance of the triple-clad fiber design is robust to mechanical stress and well suited for building very compact high power fiber lasers and amplifier sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".