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Record W2122563268 · doi:10.1002/jnm.660

Full‐wave‐based transmission‐line model for lossy‐substrate multiconductor interconnects

2007· article· en· W2122563268 on OpenAlexafffund
Behzad Kordi, Greg E. Bridges, Joe LoVetri, John E. Nordstrom

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2007
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsResearch ManitobaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaManitoba Hydro
KeywordsTransmission lineCoplanar waveguideLossy compressionSubstrate (aquarium)ConductorMaterials scienceDielectricEtching (microfabrication)Dispersion (optics)Electronic engineeringLine (geometry)Electric power transmissionEquivalent circuitOptoelectronicsTime domainAcousticsOpticsElectrical engineeringComputer sciencePhysicsTelecommunicationsEngineeringMicrowaveVoltageMathematicsComposite materialGeometry

Abstract

fetched live from OpenAlex

Abstract A full‐wave‐based modal analysis is used for simulating a multiconductor coplanar waveguide (CPW) over a selectively etched lossy silicon substrate. Propagating modes, which are similar to the classic ‘common’ and ‘differential’ modes, are extracted, and circuit theory energy relationships are used for the determination of transmission‐line model parameters. A time‐frequency domain technique is employed for implementing the transmission‐line model within a circuit simulator. The model is used to study the effect of etching the dielectric and the substrate for a two‐conductor CPW line. The simulation results show that etching both the dielectric and the lossy substrate improves the loss and dispersion characteristics of the CPW line. Copyright © 2007 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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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
GenreMethods

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
Published2007
Admission routes2
Has abstractyes

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