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Record W4285294600 · doi:10.1109/led.2022.3179228

Mitigating Tunneling Leakage in Ultrascaled HfS<sub>2</sub> pMOS Devices With Uniaxial Strain

2022· article· en· W4285294600 on OpenAlexafffund
Manasa Kaniselvan, Mayuri Sritharan, Youngki Yoon

Bibliographic record

VenueIEEE Electron Device Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicFerroelectric and Negative Capacitance Devices
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsPMOS logicNMOS logicQuantum tunnellingStrain engineeringCMOSEffective mass (spring–mass system)PhysicsDegenerate energy levelsMaterials scienceSubthreshold conductionElectrical engineeringTopology (electrical circuits)Condensed matter physicsOptoelectronicsTransistorEngineeringQuantum mechanicsVoltageSilicon

Abstract

fetched live from OpenAlex

Monolayer HfS2is promising for an all-2D CMOS platform due to its well-balanced n- and p-type transport properties, and is predicted to show high drive currents into the sub-10-nm channel length regime. However, the OFF-state performance of the ultrascaled HfS2pMOS device is limited by direct source-drain tunneling (SDT) due to carriers in the degenerate light-hole band. Here we use full-band quantum transport simulations to show that this effect can be mitigated through the use of uniaxial tensile strain, which splits the degeneracy of the valence bands and tunes the effective mass in the transport direction to suppress SDT. Strain of 1-2% applied in the transport direction reduces the subthreshold swing by over 20 mV/dec, which can improve the ON current by over 50% at channel lengths below 7 nm. This approach can be further used to tune the HfS2pMOS device to match the nMOS characteristics at a similar channel length.

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 score0.004

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)0.0010.000

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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