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Record W2762966976 · doi:10.1109/led.2017.2763120

Assessment of Germanane Field-Effect Transistors for CMOS Technology

2017· article· en· W2762966976 on OpenAlexafffund
Yiju Zhao, AbdulAziz AlMutairi, Youngki Yoon

Bibliographic record

VenueIEEE Electron Device Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSTransistorField-effect transistorPower–delay productInverterMaterials scienceVoltageOptoelectronicsThreshold voltageElectrical engineeringLogic gateTopology (electrical circuits)Engineering

Abstract

fetched live from OpenAlex

Using self-consistent atomistic quantum transport simulations, the device characteristics of n-type and p-type germanane (GeH) field-effect transistors (FETs) are evaluated. While both devices exhibit near-identical off-state characteristics, n-type GeH FET shows ~40% larger on current than the p-type counterpart, resulting in faster switching speed and lower power-delay product. Our benchmark of GeH FETs against similar devices based on 2D materials reveals that GeH outperforms MoS2and black phosphorus in terms of energy-delay product (EDP). In addition, the performance of GeH-based CMOS circuit is analyzed using an inverter chain. By engineering power supply voltage and threshold voltage simultaneously, we find the optimal operating condition of GeH FETs, minimizing EDP in the CMOS circuit. Our comprehensive study, including material parameterization, device simulation, and circuit analyses, demonstrates significant potential of GeH FETs for 2D-material CMOS circuit applications.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.013
GPT teacher head0.347
Teacher spread0.334 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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