Polarization-dependence in multiquantum well lasers and semiconductor optical amplifiers: probing interwell transport effects
Bibliographic record
Abstract
Summary form only given. The advantages and opportunities associated with the use of multiple quantum wells (MQWs) in semiconductor laser and semiconductor optical amplifier (SOA) active regions are accompanied by challenges. In a system with many wells separated by spatially thick or energetically high barriers, injected nonequilibrium carriers may become nonuniformly distributed among the wells. The subject of nonuniform carrier density distributions has attracted significant attention because it results in degradation in both static and dynamic performance: increased threshold current, above-threshold efficiency roll-off, lowered dynamic performance and worsened chirp. While the mechanisms underlying the problem of interwell carrier density nonuniformity have been clarified via numerical modeling, the phenomenon remains to be observed directly via experiment. We propose a method of using the difference in the dependence of TE vs. TM modal gain on nonuniformity of carrier density as a direct experimental probe of interwell transport effects. We illustrate the proposed mechanism: due to the difference in the spatial configurations of TE and TM optical modes, the TE and TM modes sample differently the effects of a nonuniform distribution of carriers among the wells.
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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".