Band structure and the optical gain of GaInNAs/GaAs quantum wells modeled within 10-band and 8-band <i>kp</i> model
Bibliographic record
Abstract
The band structure and optical gain have been calculated for GaInNAs/GaAs quantum wells (QWs) with various nitrogen concentrations within the 10-band and 8-band kp models. Two approaches to calculate optical properties of GaInNAs/GaAs QWs have been compared and discussed in the context of available material parameters for dilute nitrides and the conduction band nonparabolicity due to the band anti-crossing (BAC) interaction between the N-related resonant level and the conduction band of a host material. It has been clearly shown that this nonparabolicity can be neglected in optical gain calculations since the dispersion of conduction band up to the Femi level is very close to parabolic for carrier concentrations typical for laser operation, i.e., 5 × 1018 cm−3. This means that the 8-band kp model when used to calculate the optical gain is very realistic and much easier to apply in QWs containing new dilute nitrides for which the BAC parameters are unknown. In such an approach, the energy gap and electron effective mass for N-containing materials are needed, instead of BAC parameters. These parameters are available experimentally much easier than BAC parameters.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".