A passive mode-locked InAs/InP quantum dot laser with pulse duration of less than 300 fs
Bibliographic record
Abstract
We have designed and fabricated a quantum dot (QD) gain medium which consists of InAs QDs in an InGaAsP matrix on an InP substrate. By using these InAs/InP QD layers, we have generated femtosecond (fs) pulses with pulse duration of 295 fs from a single-section monolithic Fabry-Perot (F-P) cavity at the repetition rate of 50 GHz around 1560 nm wavelength range without any external pulse compression. The average output power is 40.1 mW at the injection current of 200 mA. Optical signal-to-noise ratio (OSNR) of the proposed QD mode-locked laser (QD-MLL) is up to 50 dB. The lasing threshold current and the external differential quantum efficiency are 23 mA and 30 %, respectively. And the mode beating linewidth was measured to be less than 20 KHz. We have interpreted that several nonlinear optical effects related to interaction of QD excitons with intracavity laser fields could create nonlinear dispersion to compensate intracavity linear dispersion. So total dispersion is minimized and four-wave mixing (FWM) is dramatically enhanced within QD F-P cavity. If spectral bandwidth is broad enough, tens or hundreds of longitudinal modes would lase and their phases would be locked together through FWM process. Eventually a train of fs pulses with a repetition rate corresponding to cavity round-trip time is generated.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".