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Record W3038517674 · doi:10.1039/d0nr02170a

Sub-5 nm monolayer germanium selenide (GeSe) MOSFETs: towards a high performance and stable device

2020· article· en· W3038517674 on OpenAlexaff
Ying Guo, Feng Pan, Gaoyang Zhao, Yajie Ren, Binbin Yao, Hong Li, Jing Lü

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMinistry of Education and Child Care
FundersEducation Department of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsPhosphoreneZigzagMaterials scienceGermaniumOptoelectronicsTransistorMOSFETMonolayerField-effect transistorSemiconductorElectron mobilitySelenideNanotechnologySiliconElectrical engineeringEngineeringSelenium

Abstract

fetched live from OpenAlex

Two-dimensional (2D) black phosphorene (BP) field-effect transistors (FETs) show excellent device performance but suffer from serious instability under ambient conditions. Isoelectronic 2D germanium selenide (GeSe) shares many similar properties with 2D BP, such as high carrier mobility and anisotropy, but is stable under ambient conditions. Herein, we explore the quantum transport properties of sub-5 nm ML GeSe MOSFETs using first-principles quantum transport simulation. A p-type (zigzag-directed) device is superior to other types (n- and p-type armchair-directed and n-type zigzag-directed). The on-state current of p-type devices (zigzag-directed), even at a 1 nm gate-length, can fulfill the requirements of high-performance applications for the next decade in the International Technology Roadmap for Semiconductors (ITRS, 2013 version). To the best of our knowledge, these ML GeSe MOSFETs have the smallest gate-length that can fulfill the ITRS HP on-state current requirements among reported 2D material FETs.

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 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 score0.036
Threshold uncertainty score0.725

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)0.0000.001

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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.

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

Citations48
Published2020
Admission routes1
Has abstractyes

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