Extremely Efficient DFB Lasers with Flat-Top Intra-Cavity Power Distribution in Highly Erbium-Doped Fibers
Bibliographic record
Abstract
High-performance erbium-doped DFB fiber lasers are presently required for several sensing applications, whilst the current efficiency record is only a few percent. Additionally, a flat-top intra-cavity power distribution that is not provided in traditional DFB lasers is preferred. Moreover, cavity lengths of <20 cm are attractive for fabrication and packaging. These goals can be achieved using highly erbium-doped fiber (i.e., 110 dB/m absorption at 1530 nm), providing high gain with proper engineering of coupling coefficients. In this paper, for a given background fiber loss, first the optimum intra-cavity signal powers for various pump powers are numerically calculated. Then, for a fully unidirectional laser, optimum coupling profiles are determined. Design diagrams, including contour maps for optimum cavity lengths, maximum output powers, maximum intra-cavity signal powers, and quality factors considering various pump powers and background fiber losses, are presented. The laser pump and intra-cavity signal distribution are also calculated for a realistic, feasible modified coupling profile considering a strong unidirectionality. The DFB laser is finally simulated using generalized coupled-mode equations for such modified profiles. The efficiency of more than 22% can be realized, which is the highest reported for DFB lasers based only on erbium-doped fiber.
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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 | 0.001 | 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".