Realization and optimization of a 1 ns pulsewidth multi-stage 250 kW peak power monolithic Yb doped fiber amplifier at 1064 nm
Bibliographic record
Abstract
We present a simple way to achieve and optimize hundreds of kW peak power pulsed output using a monolithic amplifier chain based on solid core double cladding fiber tightly packaged. A fiber pigtailed current driven diode is used to produce nanosecond pulses at 1064 nm. We present how to optimize the use of Fabry-Perot versus DFB type diode along with the proper wavelength locking using a fiber Bragg grating. The optimization of the two pre-amplifiers with respect to the pump wavelength and Yb inversions is presented. We explain how to manage ASE using core and cladding pumping and by using single pass and double pass amplifier. ASE rejection within the Yb fiber itself and with the use of bandpass filter is discussed. Maximizing the amplifier conversion efficiency with regards to the fiber parameters, glass matrix and signal wavelength is described in details. We present how to achieve high peak power at the power amplifier stage using large core/cladding diameter ratio highly doped Yb fibers pumped at 975 nm. The effect of pump bleaching on the effective Yb fiber length is analyzed carefully. We demonstrate that counter-pumping brings little advantage in very short length amplifier. Dealing with the self-pulsation limit of stimulated Brillouin scattering is presented with the adjustment of the seed pulsewidth and linewidth. Future prospects for doubling the output peak power are discussed.
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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 | 0.001 | 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".