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Record W3094410293 · doi:10.1109/ims30576.2020.9223829

Surface-Volume-Surface EFIE for Analysis of 3-D Microwave Circuits in Multilayered Substrates With Finite Dielectric Inclusions

2020· article· en· W3094410293 on OpenAlexaff
Shucheng Zheng, Reza Gholami, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDielectricDiplexerIntegral equationMicrowaveElectric-field integral equationMaterials sciencePlanarElectronic engineeringSurface (topology)Electronic circuitComputational electromagneticsMicrostripMathematical analysisElectromagnetic fieldMathematicsComputer sciencePhysicsOptoelectronicsEngineeringTelecommunicationsGeometryElectrical engineering

Abstract

fetched live from OpenAlex

Novel formulation of the Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) for rigorous full-wave electromagnetic analysis of composite metal-dielectric structures embedded in planar multilayered medium is proposed. Handling of multilayered medium dyadic Green's function (DGF) is based on Michalski-Zheng's mixed-potential formulation and does not require introduction of any additional components compared to those featured in the traditional mixed-potential integral equation (MPIE) formulations for the analysis of metal structures in layered medium. Characterization of microwave circuits and interconnect structures embedded in dielectric substrates featuring finite dielectric inclusions are among applications well suited for handling with the new single source integral equation formulation. Proposed methodology is validated through comparison of extracted network parameters of realistic 3D model of LTCC diplexer against those obtained with a commercial electromagnetic analysis tool.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.031
GPT teacher head0.246
Teacher spread0.215 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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