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Record W2023964354 · doi:10.4236/jemaa.2013.51005

Efficient Time-Domain Signal and Noise FET Models for Millimetre-Wave Applications

2013· article· en· W2023964354 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

VenueJournal of Electromagnetic Analysis and Application · 2013
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceNoise (video)Time domainSIGNAL (programming language)Transmission lineElectronic engineeringTransistorAmplifierEquivalent circuitFrequency domainTransmission (telecommunications)Distributed element modelLow-noise amplifierElectrical engineeringTelecommunicationsVoltageEngineering

Abstract

fetched live from OpenAlex

Based on the active coupled line concept, a novel approach for efficient signal and noise modeling of millimeter-wave field-effect transistors is proposed. The distributed model considers the effect of wave propagation along the device electrodes, which can significantly affect the device performance especially in the millimetre-wave range. By solving the multi-conductor transmission line equations using the Finite-Difference Time-Domain technique, the proposed procedure can accurately determine the signal and noise performance of the transistor. In order to demonstrate the proposed FET model accuracy, a distributed low-noise amplifier was designed and tested. A model selection is often a trade-off between procedure complexity and response accuracy. Using the proposed distributed model versus the circuit-based model will allow increasing the model frequency range.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.528

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 head0.188
Teacher spread0.183 · 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