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Record W2011693928 · doi:10.1109/jqe.2003.823024

Galerkin Method for Calculating Valence-Band Wavefunctions in Quantum-Well Structures Using Exact Envelope Theory

2004· article· en· W2011693928 on OpenAlexaff
Gordon Morrison, Sean C. Woodworth, Huamiao Wang, Daniel T. Cassidy

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGalerkin methodWave functionEnvelope (radar)Discontinuous Galerkin methodBoundary value problemMathematical analysisMathematicsPhysicsApplied mathematicsQuantum mechanicsFinite element methodComputer science

Abstract

fetched live from OpenAlex

The ability to calculate accurately the valence-band structure in semiconductors is important in the design of quantum-well (QW) semiconductor devices. The Galerkin method for calculating accurate analytic approximations for multicomponent valence-band wavefunctions is computationally fast and efficient. The Galerkin method is, in fact, an improved version of an earlier Raleigh-Ritz-type variational method. In this paper, we remark that both the variational method and the more recently proposed Galerkin method are formulated such that they imply symmetrized boundary constraints at material interfaces, with the symmetrized nature of the constraints arising from neglecting the ordering of the operators. Burt's exact envelope-function theory for semiconductor microstructures has been used to demonstrate, however, that the commonly used symmetrized boundary constraints for material interfaces are unphysical. We therefore present a modified version of the Galerkin method that implicitly assumes physically reasonable, exact envelope-function boundary constraints. Simulations show that the modified Galerkin method successfully produces physical, semi-analytic results that are consistent with exact envelope theory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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 designTheoretical or conceptual
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

Citations11
Published2004
Admission routes1
Has abstractyes

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