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Record W1486706724 · doi:10.1002/jnm.2090

A new approach for signal and noise FET modeling including wave propagation effects

2015· article· en· W1486706724 on OpenAlexaff
Shahrooz Asadi, Mustapha C. E. Yaghoub, Sadegh Biabanifard

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsExtremely high frequencyNoise (video)Field-effect transistorElectronic engineeringRadio propagationComputer scienceField (mathematics)TransistorElectrical engineeringTelecommunicationsAcousticsPhysicsEngineeringVoltage

Abstract

fetched live from OpenAlex

Abstract In the millimeter‐wave frequency range, electromagnetic effects can significantly influence a device behavior. As the core of modern communication systems, active devices such as field‐effect transistors require up‐to‐date models to accurately integrate such effects, especially in terms of noise performance because most of communication systems operate in noisy environments. Based on the active coupled line concept, a novel approach for efficient noise performance modeling of millimeter‐wave field‐effect transistors is proposed. This distributed model considers the effect of wave propagation along the device electrodes, which can significantly affect the noise performance, especially in the millimeter‐wave range. Copyright © 2015 John Wiley & Sons, Ltd.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.248
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations12
Published2015
Admission routes1
Has abstractyes

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