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Record W4390005167 · doi:10.1063/5.0179100

Analyzing k · p modeling in highly mismatched alloys and other III–V semiconductors

2023· article· en· W4390005167 on OpenAlexaff
M. Gładysiewicz, Marek S. Wartak

Bibliographic record

VenueJournal of Applied Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSemiconductorBrillouin zoneBand gapWide-bandgap semiconductorCondensed matter physicsElectronic band structureContext (archaeology)SemimetalValence bandDirect and indirect band gapsSemiconductor materialsDiamondElectronic bandMaterials sciencePhysicsOptoelectronics

Abstract

fetched live from OpenAlex

This Tutorial provides a comprehensive overview of various k⋅p models used to describe the electronic band structures of semiconductors with cubic diamond and zinc blende symmetries. Our primary focus is on III–V semiconductors, with a particular emphasis on highly mismatched alloys. We begin our exploration with the six-band k⋅p model, which effectively captures interactions within the highest valence bands. Following that, we delve into the intricacies of the eight-band k⋅p model, which takes into account strain effects and modifications to energy dispersion. The Tutorial also introduces the band anticrossing model and its corresponding ten-band k⋅p models, specifically tailored for dilute nitride semiconductors. Furthermore, we extend our discussion to the valence band anticrossing model and its application to the 14-band k⋅p model in the context of dilute bismide materials. Additionally, we emphasize the significance of more comprehensive models, exemplified by the 30-band k⋅p model, for faithfully representing the entire Brillouin zone.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.236
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

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