Gain measurements of Fabry-Pe/spl acute/rot semiconductor lasers using a nonlinear least-squares fitting method
Bibliographic record
Abstract
A method for the measurement of the gain-reflectance product of Fabry-Pe/spl acute/rot (F-P) semiconductor lasers is proposed and compared to other techniques. The method is based on a nonlinear, least-squares fitting of the F-P modes to an Airy function. A separate fitting is performed over each mode, as measured with an optical spectrum analyzer (OSA), so that the gain-reflectance parameters are extracted. The influence of the OSAs response function is considered by convolution of the Airy function with the response function of the OSA. By comparing with the Hakki-Paoli method, the mode sum/min method, and the Fourier series expansion method, we find that the nonlinear fitting method is the least sensitive to noise. However, owing to a broadening of the F-P modes of the semiconductor laser, the mode sum/min method combined with a deconvolution technique gives the least underestimated gain above threshold.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".