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Record W2958562926 · doi:10.1109/ted.2019.2924170

Modeling of Ballistic Monolayer Black Phosphorus MOSFETs

2019· article· en· W2958562926 on OpenAlexafffund
Raphaël J. Prentki, Fei Liu, Hong Guo

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2019
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcGill University
FundersCompute CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDrain-induced barrier loweringMOSFETMaterials scienceOptoelectronicsBallistic conductionTransistorMonolayerField-effect transistorCapacitorVoltageCondensed matter physicsElectrical engineeringNanotechnologyPhysicsEngineeringElectron

Abstract

fetched live from OpenAlex

In this paper, we present two accurate physics-based models of ballistic metal-oxide-semiconductor field-effect transistors (MOSFETs), both using less than ten parameters. These models-the capacitor model and the virtual source model-are based on the Landauer-Büttiker formalism. We show that the nonthermalization of charge carriers in the channels of ballistic MOSFETs leads to two critical effects that need to be considered in the modeling: the ballistic drain-induced barrier lowering (DIBL) effect and the floating source effect. The ballistic DIBL effect is responsible for a drain voltage dependence of the DIBL parameter; the floating source effect intensifies the injection of high-energy carriers from the source as the gate voltage increases. Specifically, the analysis is carried out on devices composed of monolayer black phosphorus, a 2-D semiconductor with unique electronic and mechanical properties, which make it a promising candidate for 2-D digital logic applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.001

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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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.

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

Citations10
Published2019
Admission routes2
Has abstractyes

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