8-band and 14-band <i>kp</i> modeling of electronic band structure and material gain in Ga(In)AsBi quantum wells grown on GaAs and InP substrates
Bibliographic record
Abstract
The electronic band structure and material gain have been calculated for GaAsBi/GaAs quantum wells (QWs) with various bismuth concentrations (Bi ≤ 15%) within the 8-band and 14-band kp models. The 14-band kp model was obtained by extending the standard 8-band kp Hamiltonian by the valence band anticrossing (VBAC) Hamiltonian, which is widely used to describe Bi-related changes in the electronic band structure of dilute bismides. It has been shown that in the range of low carrier concentrations n &lt; 5 × 1018 cm−3, material gain spectra calculated within 8- and 14-band kp Hamiltonians are similar. It means that the 8-band kp model can be used to calculate material gain in dilute bismides QWs. Therefore, it can be applied to analyze QWs containing new dilute bismides for which the VBAC parameters are unknown. Thus, the energy gap and electron effective mass for Bi-containing materials are used instead of VBAC parameters. The electronic band structure and material gain have been calculated for 8 nm wide GaInAsBi QWs on GaAs and InP substrates with various compositions. In these QWs, Bi concentration was varied from 0% to 5% and indium concentration was tuned in order to keep the same compressive strain (ε = 2%) in QW region. For GaInAsBi/GaAs QW with 5% Bi, gain peak was determined to be at about 1.5 μm. It means that it can be possible to achieve emission at telecommunication windows (i.e., 1.3 μm and 1.55 μm) for GaAs-based lasers containing GaInAsBi/GaAs QWs. For GaInAsBi/Ga0.47In0.53As/InP QWs with 5% Bi, gain peak is predicted to be at about 4.0 μm, i.e., at the wavelengths that are not available in current InP-based lasers.
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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".