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Record W2031990085 · doi:10.1063/1.3671022

The role of vicinal silicon surfaces in the formation of epitaxial twins during the growth of III-V thin films

2011· article· en· W2031990085 on OpenAlexafffund
Gabriel A. Devenyi, Steffi Y. Woo, Shahram Ghanad-Tavakoli, Robert A. Hughes, R. N. Kleiman, Gianluigi A. Botton, J. S. Prestón

Bibliographic record

VenueJournal of Applied Physics · 2011
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials Research
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityOntario Centres of Excellence
KeywordsVicinalSiliconEpitaxyMaterials scienceCrystal twinningThin filmSemiconductorTransmission electron microscopyTilt (camera)CrystallographyOptoelectronicsCondensed matter physicsNanotechnologyChemistryMicrostructureComposite material

Abstract

fetched live from OpenAlex

We examine the role of vicinal surface steps in the formation and propagation of twins during the growth of epitaxial III-V thin films (GaAs, InP, GaSb, AlSb) on silicon substrates. This is achieved through the combined use of two-dimensional X-ray diffraction and conventional transmission electron microscopy techniques, which allow for both a macro and nano/micro characterization of the material systems. Observed is a systematic suppression of twins formed opposite to the tilt direction of vicinal substrates through a process of step-flow overgrowth of nucleated twins, and an enhancement of twins toward the tilt direction when the fastest growth planes are aligned with the step-flow. These results indicate a probable path to the enhancement of the electronic mobility of lateral devices based on III-V semiconductors on silicon.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

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