Widely tunable and switchable multiwavelength erbium-doped fiber laser based on a single ring cavity
Bibliographic record
Abstract
In this paper, we propose and demonstrate through numerical simulations a novel, to the best of our knowledge, widely tunable and switchable multiwavelength erbium-doped fiber laser (EDFL) operating in the 1524–1650 nm wavelength range covering the C-, L-, and U-bands. The EDFL is based on a single ring cavity and a single forward pump source operating at 980 nm wavelength. The length of the erbium-doped fiber and pumping configuration is optimized, enabling a wide tuning of 126 nm with an average lasing power of 15 dBm. Switchable multiple wavelengths are generated from the EDFL through an intracavity tunable optical filter (TOF) and a specially designed out-of-cavity comb filter (CF). Forty-nine wavelengths having average optical power and linewidth of around −10 dBm and 0.01 nm, respectively, are obtained at the output of the CF. Moreover, the free spectral range among the multiple wavelengths is adjusted easily by simply varying the frequency of sinusoidal radio frequency signal used to modulate the lasing signal obtained by tuning the intracavity TOF at a particular wavelength with the help of a dual-drive Mach–Zehnder modulator. The proposed EDFL is low cost, efficient, and highly stable with an average output power of around 15 dBm for 10% output coupling ratio over a 126 nm wide tuning range covering C-, L- and U-bands.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".