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Record W2136234499 · doi:10.1109/ted.2005.850656

MOSFET Modeling for RF IC Design

2005· article· en· W2136234499 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

VenueIEEE Transactions on Electron Devices · 2005
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMOSFETElectronic engineeringRadio frequencyElectrical engineeringComputer scienceEngineeringVoltageTransistor

Abstract

fetched live from OpenAlex

High-frequency (HF) modeling of MOSFETs for radio-frequency (RF) integrated circuit (IC) design is discussed. Modeling of the intrinsic device and the extrinsic components is discussed by accounting for important physical effects at both dc and HF. The concepts of equivalent circuits representing both intrinsic and extrinsic components in a MOSFET are analyzed to obtain a physics-based RF model. The procedures of the HF model parameter extraction are also developed. A subcircuit RF model based on the discussed approaches can be developed with good model accuracy. Further, noise modeling is discussed by analyzing the theoretical and experimental results in HF noise modeling. Analytical calculation of the noise sources has been discussed to understand the noise characteristics, including induced gate noise. The distortion behavior of MOSFET and modeling are also discussed. The fact that a MOSFET has much higher "low-frequency limit" is useful for designers and modelers to validate the distortion of a MOSFET model for RF application. An RF model could well predict the distortion behavior of MOSFETs if it can accurately describe both dc and ac small-signal characteristics with proper parameter extraction.

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 categoriesMeta-epidemiology (narrow)
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.244
Teacher spread0.216 · 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