Efficient 1018nm high power fiber laser using intracavity tilted FBG ASE filters
Bibliographic record
Abstract
We present an efficient way to remove unwanted Amplified Stimulated Emission (ASE) in high-power fiber lasers and amplifiers using intracavity Chirped Tilted Fiber Bragg Grating (CTFBG) filters. The grating is written with tilted fringes so that the unwanted ASE is reflected into the fiber cladding where it is no longer amplified. Depending on the desired emission wavelength and active fiber, one or several filters are spliced within the active fiber to suppress ASE before it reaches a detrimental power. Numerical simulations clearly show that adding the filters allows amplification in configurations that would just be impossible due to the onset of ASE. The filter bandwidth and extinction, and the maximum allowed active fiber length between each filter are also computed depending on the core/cladding diameter ratio of the active fiber used and the targeted emission wavelength. As an example, a fiber laser at 1018 nm is assembled in a 20/400μm core/cladding diameter ytterbium fiber that is cladding pumped at 976 nm. Two CTFBGs with 20 dB attenuation from 1025 nm to 1070 nm are spliced within the 6-meter-long ytterbium fiber. 432 W of laser emission at 1018 nm is efficiently achieved at 77% slope efficiency with respect to the absorbed pump power. The extinction between the 1018 nm signal and the ASE is greater than 50 dB. Removing the ASE filters from the cavity clearly leads to only self-pulsation of the ASE between 1030 nm and 1050 nm, no generation of 1018 nm light was possible. The measured thermal slope of the filters shows scalability above the kW level. Demonstration at 1908 nm with a 25/400 core/cladding diameter thulium doped fiber is also done. Tests were done to inscribe the CTFBG directly in an ytterbium fiber for simpler implementation and avoid additional splicing.
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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 | 0.000 | 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".