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Record W2767849965 · doi:10.1109/ted.2017.2766782

Channel Material Dependence of Wave Function Deformation Scattering in Ultrascaled FinFETs

2017· article· en· W2767849965 on OpenAlexafffund
Michael Wong, Kyle D. Holland, Ji Kai Wang, Thomas Cam, Terence B. Hook, Diego Kienle, Prasad S. Gudem, Mani Vaidyanathan

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesInternational Business Machines Corporation
KeywordsScatteringChannel (broadcasting)Coupling (piping)Materials scienceDeformation (meteorology)Effective mass (spring–mass system)Surface finishEnhanced Data Rates for GSM EvolutionCondensed matter physicsFunction (biology)MechanicsPhysicsOpticsComposite materialClassical mechanicsTelecommunications

Abstract

fetched live from OpenAlex

We investigate the channel material dependence of wave function deformation scattering (WDS), a phenomenon that occurs when the shape of the carrier wave function is forced to change as the channel is traversed. Line-edge roughness (LER) is one nonideality that can induce WDS in confined device geometries. We perform nonequilibrium Green's function simulations of ensembles of ultrascaled Fin Field Effect Transistors that exhibit correlated LER to determine the resulting on-current distributions. By considering various channel materials, we demonstrate two trends. First, WDS has a greater impact when the transport effective mass of the channel material is low, due to stronger coupling between conducting subbands. Second, WDS has a greater impact when the confinement effective mass of the channel material is high, due to the presence of more conducting subbands, which further enhances coupling.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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 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

Citations0
Published2017
Admission routes2
Has abstractyes

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