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Record W4241514372 · doi:10.35940/ijitee.b1070.1292s19

Modelling and Simulation of Tri-layered (s-Si/s-SiGe/s-Si) Channel Double Gate Nano FET

2019· article· en· W4241514372 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Innovative Technology and Exploring Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceOptoelectronicsMiniaturizationTransistorMOSFETShort-channel effectSubthreshold slopeField-effect transistorSubthreshold conductionSiliconThreshold voltageGate oxideDrain-induced barrier loweringReverse short-channel effectElectron mobilityChannel (broadcasting)Leakage (economics)Electrical engineeringNanotechnologyVoltageEngineering

Abstract

fetched live from OpenAlex

The down scaling of Meatal Oxide Semiconductor Field Effect transistor (MOSFET) devices nevertheless the most important and effective way for accomplishing high performance with low power adopted the miniaturization trend of channel length from the past, which is very aggressive. The double gate NanoFET with the incorporation of the strain Silicon technology is developed here on 45nm gate length comprises of tri-layered (s-Si/s-SiGe/s-Si) channel region with varied thicknesses. The induction of strain increases mobility of charge carriers. Two gates are deployed in bottom and up side of strained channel provides better control over the depletion region developed by applying same gate bias voltage. This newly developed double gate NanoFET on 45nm channel length provides 63% reduced subthreshold leakage current, and maximum electron drift velocity in strained channel.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.258
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