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Record W2508917673 · doi:10.1109/ted.2016.2602209

Switching-Speed Limitations of Ferroelectric Negative-Capacitance FETs

2016· article· en· W2508917673 on OpenAlexafffund
Zhi Cheng Yuan, Shahriar Rizwan, Michael Wong, Kyle D. Holland, S.E. Anderson, Terence B. Hook, Diego Kienle, S.M. GadelRab, Prasad S. Gudem, Mani Vaidyanathan

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2016
Typearticle
Languageen
FieldEngineering
TopicFerroelectric and Negative Capacitance Devices
Canadian institutionsQualcomm (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsFerroelectricityCapacitanceNegative impedance converterScalingMaterials scienceVoltageSubthreshold conductionSwitching timeCondensed matter physicsOptoelectronicsPhysicsTransistorElectrical engineeringEngineeringElectrodeVoltage dividerMathematicsDielectricQuantum mechanics

Abstract

fetched live from OpenAlex

Recently, negative-capacitance FETs (NCFETs) have been proposed to reduce subthreshold slope and help continue supply-voltage scaling alongside channel-length scaling. We investigate the high-frequency switching behavior of NCFETs using the Landau-Khalatnikov equation to model ferroelectric materials. Multidomain interactions in the ferroelectric are considered, resulting in strong agreement with experimental measurements. Operation of NCFETs at gigahertz frequencies is investigated with this experimentally validated multidomain model. We find that the effectiveness of the voltage amplification in NCFETs is strongly dependent on the viscosity coefficient ρ of the ferroelectric, and that a low ρ (<;0.1 Ω · m) is required for the operation at the high gigahertz frequencies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0000.001
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 head0.028
GPT teacher head0.226
Teacher spread0.198 · 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

Citations85
Published2016
Admission routes2
Has abstractyes

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