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Record W2764209371 · doi:10.1109/jqe.2017.2762400

An Accurate Method of Modeling Self-Assembled InAs/InGaAsP/InP (001) Quantum Dot With Double-Capping Procedure

2017· article· en· W2764209371 on OpenAlexaff
Yiling Xiong, Xiupu Zhang

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsConcordia University
Fundersnot available
KeywordsQuantum dotPhotoluminescenceMaterials scienceExcitonSubstrate (aquarium)Gallium arsenideCondensed matter physicsOptoelectronicsQuantum dot laserMolecular physicsPhysicsSemiconductorSemiconductor laser theory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.330
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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