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Record W3195685817 · doi:10.1063/5.0053789

Strain-tuning PtSe2 for high ON-current lateral tunnel field-effect transistors

2021· article· en· W3195685817 on OpenAlexafffund
Manasa Kaniselvan, Youngki Yoon

Bibliographic record

VenueApplied Physics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum tunnellingMaterials scienceTransistorField-effect transistorOptoelectronicsMonolayerTunnel field-effect transistorStrain engineeringEffective mass (spring–mass system)Subthreshold slopeChannel length modulationIonMOSFETVoltageCondensed matter physicsNanotechnologyElectrical engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

We use full-band quantum transport simulations to show that monolayer platinum diselenide (PtSe2) tunnel field-effect transistors (TFETs) can deliver high ON currents (ION) under biaxial tensile strain, while maintaining a sub-60 mV/dec subthreshold swing. When strained, monolayer PtSe2 develops a lower effective mass and a small gap across which an efficient tunneling can occur, translating to a high ION when used in a TFET channel. At a drain voltage of 0.8 V and OFF current of 1×10−7 μA/μm, a simulated device with a 5% strained channel has an ION > 116 μA/μm, which is three orders of magnitude greater than that of the unstrained unoptimized device. The corresponding I60 is also increased by 600 times. This improvement comes at a reasonable cost of degradation in the OFF state and has a minimal effect on the switching characteristics down to 10 nm channel length. Our results present the mechanical flexibility of 2D materials as a powerful tuning parameter toward their use in high-performance tunneling devices.

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 categoriesMeta-epidemiology (narrow)
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 score0.248
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)0.0000.000

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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