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Record W3112323218 · doi:10.1109/ted.2020.3041216

Negative-Capacitance FET With a Cold Source

2020· article· en· W3112323218 on OpenAlexafffund
Shujin Guo, Raphaël J. Prentki, Kexin Jin, Changle Chen, Hong Guo

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2020
Typearticle
Languageen
FieldEngineering
TopicFerroelectric and Negative Capacitance Devices
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsField-effect transistorCapacitanceFerroelectricityTransistorPhysicsMaterials scienceOptoelectronicsAnalytical Chemistry (journal)Electrical engineeringQuantum mechanicsChemistryElectrodeDielectricEngineering

Abstract

fetched live from OpenAlex

The subthreshold swing (SS) of a field-effect transistor (FET) is given by the body factor multiplied by the transport factor and has a limit of$60\text{ mV} \cdot ^{-1}$at room temperature in the case of the MOSFET. To break this SS limit, the negative-capacitance FET (NC-FET) lowers the body factor by using a ferroelectric film in the gate stack, whereas the cold source FET (CS-FET) and the Dirac source FET (DS-FET) lower the transport factor by introducing an electronic bandgap or manipulating the density of states in the injecting source. In this work, we theoretically and computationally investigate the possibility of FETs with both NC and CS/DS so that both the body and transport factors are lowered simultaneously. The new device physics of the negative-capacitance CS-FET (NCCS-FET) is numerically investigated for 2-D monolayer black phosphorus (ML-BP) FETs with the Hf0.5Zr0.5O3ferroelectric material in the gate stack. The device characteristics of six different FETs, the conventional MOSFET, CS-FET, DS-FET, NC-FET, NCCS-FET, and NCDS-FET are calculated and compared. Overall, the NCCS-FET achieves an average SS of$30.1\text{ mV} \cdot ^{-1}$and a minimum SS as low as$7.21\text{ mV} \cdot ^{-1}$; its O N–O FF ratio is about four orders of magnitude higher than that of a conventional MOSFET. The combined effects of NC and CS more efficiently decrease power dissipation.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.195
Teacher spread0.184 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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