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Atomic-Scale Insights into Semiconductor Heterostructures: From Experimental Three-Dimensional Analysis of the Interface to a Generalized Theory of Interfacial Roughness Scattering

2020· article· en· W3004307392 on OpenAlexafffund
Thomas Grange, Samik Mukherjee, Giovanni Capellini, Michele Montanari, Luca Persichetti, L. Di Gaspare, Stefan Birner, Anis Attiaoui, Oussama Moutanabbir, Michele Virgilio, M. De Seta

Bibliographic record

VenuePhysical Review Applied · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsPolytechnique Montréal
FundersInstitute of Development and Economic AlternativesNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeCanada Research Chairs
KeywordsHeterojunctionMiniaturizationSemiconductorScatteringCascadeMaterials scienceSurface finishOptoelectronicsSurface roughnessAtomic unitsQuantumScale (ratio)LaserQuantum dotOpticsPhysicsNanotechnologyQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

Relentless miniaturization has driven progress in semiconductor technology, but now, at the atomic scale, predictive descriptions of heterointerfaces (and even basic data on them) are still conspicuously absent. The authors combine atom-probe tomography with advanced modeling to study the roughness of real interfaces, and their influence on charge-carrier scattering in two-dimensional quantum confined systems. This yields a state-of-the art platform to simulate the optical gain in $e.g.$ a Si-Ge quantum cascade laser, allowing precise control of optoelectronic performance by elucidating key physical properties of heterointerfaces and their impact on device physics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.279
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations50
Published2020
Admission routes2
Has abstractyes

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