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Record W4365801542 · doi:10.1109/jeds.2023.3267081

Current Prospects and Challenges in Negative-Capacitance Field-Effect Transistors

2023· article· en· W4365801542 on OpenAlexaff
Md. Sherajul Islam, Abdullah Al Mamun Mazumder, Changjian Zhou, Catherine Stampfl, Jeongwon Park, Cary Y. Yang

Bibliographic record

VenueIEEE Journal of the Electron Devices Society · 2023
Typearticle
Languageen
FieldEngineering
TopicFerroelectric and Negative Capacitance Devices
Canadian institutionsUniversity of Ottawa
FundersNational Science Foundation
KeywordsCMOSTransistorContext (archaeology)CapacitanceEngineering physicsField-effect transistorScalingNegative impedance converterSemiconductor deviceNanotechnologyElectrical engineeringElectronic engineeringMaterials scienceSemiconductorComputer sciencePhysicsOptoelectronicsEngineeringVoltage

Abstract

fetched live from OpenAlex

For decades, the fundamental driving force behind energy-efficient and cost-effective electronic components has been the downward scaling of electronic devices. However, due to approaching the fundamental limits of silicon-based complementary metal-oxide-semiconductor (CMOS) devices, various emerging materials and device structures are considered alternative aspirants, such as negative-capacitance field-effect transistors (NCFETs), for their promising advantages in terms of scaling, speed, and power consumption. In this article, we present a brief overview of the progress made on NCFETs, including theoretical and experimental approaches, a current understanding of NCFET device physics, possible physical mechanisms for NC, and future functionalization prospects. In addition, in the context of recent findings, critical technological difficulties that must be addressed in the NCFET development are also discussed.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.006
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations21
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Journal of the Electron Devices SocietySame topicFerroelectric and Negative Capacitance DevicesFrench-language works237,207