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Record W2005639145 · doi:10.1063/1.3264629

High-field hole transport in silicon nanowires

2009· article· en· W2005639145 on OpenAlexaff
Amit Verma, Andrei Buin, M. P. Anantram

Bibliographic record

VenueJournal of Applied Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversité de Montréal
FundersNational Institute of Standards and TechnologyTexas A and M UniversityNational Science Foundation
KeywordsNanowireSiliconCondensed matter physicsPhononScatteringMean free pathDrift velocityMaterials scienceMonte Carlo methodElectron mobilityElectric fieldPhonon scatteringPhysicsOptoelectronicsOptics

Abstract

fetched live from OpenAlex

We report on ensemble Monte Carlo hole transport simulations for small diameter silicon nanowires. The basis for the simulations is provided by band structure calculations using sp3d5s∗ tight-binding scheme. Principal scattering mechanisms considered are hole-bulk acoustic and optical phonon interactions. Both steady-state and transient hole transport characteristics are explored. For the silicon nanowires considered, the steady-state average hole drift velocity saturates due to optical phonon scattering. Acoustic and intersubband scattering mechanisms strongly prevent an oscillation in the hole velocity in the transient regime. Room temperature hole mean free path for the different silicon nanowires considered is evaluated to be less than 10 nm at various electric fields.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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