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Record W1991658764 · doi:10.1143/jjap.43.3831

Electrical Field Analysis of Nanoscale Field Effect Transistors

2004· article· en· W1991658764 on OpenAlexaff
Aissa Boudjella, Zhong-Fang Jin, Yvon Savaria

Bibliographic record

VenueJapanese Journal of Applied Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsPolytechnique Montréal
FundersWelch FoundationNational Science Foundation
KeywordsElectric fieldField-effect transistorField (mathematics)Nanoscopic scaleDielectricField effectAnalytical Chemistry (journal)Materials scienceIntensity (physics)Threshold voltageTransistorCondensed matter physicsVoltageChemistryNanotechnologyOptoelectronicsPhysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Numerical simulations have been performed to analyze the electric field inside nanoscale field effect transistors with channel lengths L ch of 2 and 4 nm. Our electrostatic analyses characterize the electric field distribution inside the device structure when the ratio of dielectric thickness T ox to L ch ( T ox / L ch ) ranges from 0.2 to 50. At constant drain voltage, the relationship between the gate voltage V g and T ox / L ch in the field distribution was investigated. Near the interface, the field intensity changes significantly and depends on V g , T ox / L ch and on the distance from the interface. V g has a strong effect on channel field for a small T ox / L ch (0.2–0.66). This effect decreases but remains significant when T ox / L ch increases in the range of 0.66–5. On the other hand, for T ox / L ch on the order of 5, V g has a limited impact on the channel field and becomes negligible as T ox / L ch increases up to 50. We confirmed Kagen et al. 's suggestion that the values of T ox and L ch need to be properly selected to obtain functional nanoscale field effect transistors. However, we found that the gating effect should be included in device models for much higher of T ox / L ch values. Moreover, our results approximately corresponded to related work published by Damle et al.

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 score0.065
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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