Strategies for developing tunable multiwavelength mode-locked semiconductor fiber ring lasers
Bibliographic record
Abstract
Compact optical sources that generate picosecond pulses at multiple wavelengths are of interest for numerous applications in optical instrumentation, fiber optic sensing, and optical communications. In recent years, numerous methods have been demonstrated to obtain multi-wavelength, mode-locked (ML) operation from erbium-doped fiber lasers (EDFLs) and semiconductor fiber ring lasers (SFRLs). In contrast to EDFLs, the use of semiconductor optical amplifiers (SOAs) allows for stable, multi-wavelength emission at room temperature with narrow wavelength separation since they are not constrained by the EDF homogenous broadened linewidth, and for operation over a wide wavelength band. To increase the functionality for some applications, it is also important to be able to tune the output wavelengths of the optical pulse source. In this paper, we provide an overview of our on-going work on developing tunable multi-wavelength, ML-SFRLs. In terms of achieving multi-wavelength operation, we have used multi-wavelength filters based on a high-birefringence Sagnac loop and superimposed fiber Bragg gratings. In terms of short pulse generation, we have explored two different methods for mode-locking: the use of an intra-cavity electro-optic modulator and the injection of an external optical control signal to modulate the gain of the SOA via cross-gain modulation. Finally, in terms of wavelength tunable operation, we have exploited dispersion tuning, i.e. the use of a dispersive cavity and changing the modulation frequency of the mode-locking element. We present and discuss our results for two different ML-SFRL configurations.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".