Non-linear frequency tuning of long-wavelength VCSELs: phenomenological description
Bibliographic record
Abstract
We have approximated with second-order polynomials the frequency tuning curves of long-wavelength single-mode VCSELs operating near 1654 and 1512 nm. Fitting coefficients were calculated using experimental data on injection currents and heat sink temperatures required to tune lasers to frequency markers generated by gas absorption lines. To measure temperature tuning rates, we tuned the lasers by temperature sequentially to pairs of absorption lines with known frequency separations. To determine fitting coefficients associated with linear and non-linear frequency tuning, we varied the laser injection currents and temperatures simultaneously in such a way that made a laser emit exactly the same frequency. Linear and non-linear tuning coefficients were then calculated from the data on effects of relatively small and large variation of laser operation parameters on laser frequency. Lasers were calibrated by tuning them on narrow absorption lines with frequencies accurately known from previous studies. The simulated tuning curves were demonstrated to fit frequency markers generated over spectral intervals up to 40 cm-1 with an accuracy of ± 0.10 cm-1(1654-nm laser) and ± 0.15 cm-1 (1512-nm laser). A temperature dependence of injection current tuning rates of the 1654-nm laser was determined from the best fits of simulated tuning curves to a series of CO2 absorption lines in the whole operation temperature range of the laser (0 - 50 °C). A simple and accurate method developed to describe tuning properties of long-wavelength VCSELs can be applied to quantitatively characterize any narrow-linewidth tunable laser.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".