An Accurate Method of Modeling Self-Assembled InAs/InGaAsP/InP (001) Quantum Dot With Double-Capping Procedure
Bibliographic record
Abstract
The double-capping procedure has been widely used to control the size distribution of self-assembled InAs/InGaAsP quantum dots during the growth on a (001) InP substrate. However, the conventional simulation method referred to one-step model does not include this procedure, which may lead to inaccuracy in modeling of quantum dots. An accurate method of modeling a single quantum dot including a thorough elastic strain analysis is proposed and developed in this paper. The confinement potential profiles are found significantly different between the two models. A series of settings (i.e., dot heights, dot base sizes, and dot shapes) is considered. The electronic band structure is calculated by using the eight-band $k \cdot p$ model. By comparing with the photoluminescence measurements in previously published works, it is found that the obtained optical transition energies using the accurate two-step model are in better agreement. The bright exciton splitting is found larger in terms of fine structures. Moreover, the impact of the quaternary compositions (arsenic mole fraction) of barrier material is for the first time systematically studied by using this accurate model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".