Thermally insensitive determination of the linewidth broadening factor in nanostructured semiconductor lasers using optical injection locking
Bibliographic record
Abstract
In semiconductor lasers, current injection not only provides the optical gain, but also induces variation of the refractive index, as governed by the Kramers-Krönig relation. The linear coupling between the changes of the effective refractive index and the modal gain is described by the linewidth broadening factor, which is responsible for many static and dynamic features of semiconductor lasers. Intensive efforts have been made to characterize this factor in the past three decades. In this paper, we propose a simple, flexible technique for measuring the linewidth broadening factor of semiconductor lasers. It relies on the stable optical injection locking of semiconductor lasers, and the linewidth broadening factor is extracted from the residual side-modes, which are supported by the amplified spontaneous emission. This new technique has great advantages of insensitivity to thermal effects, the bias current, and the choice of injection-locked mode. In addition, it does not require the explicit knowledge of optical injection conditions, including the injection strength and the frequency detuning. The standard deviation of the measurements is less than 15%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".