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Record W1959341122 · doi:10.1109/apmc.1999.828061

Accurate parameter extraction and joint field/circuit model of uniplanar and multi-layer microwave and millimeter wave monolithic and hybrid integrated circuits and antennas

2003· article· en· W1959341122 on OpenAlexaff
Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsElectronic circuitElectronic engineeringEquivalent circuitComputer scienceMethod of moments (probability theory)Extremely high frequencyField (mathematics)MicrowaveMicrostripElectrical elementMonolithic microwave integrated circuitEngineeringElectrical engineeringTelecommunicationsMathematicsAmplifier

Abstract

fetched live from OpenAlex

Unified joint field/circuit model is proposed and developed for accurate representation of uniplanar and multilayer integrated circuits. It is realized through the use of a newly proposed parameter extraction scheme that is called "short-open calibration" (SOC) and it is self-contained in a full-wave method of moments (MoM). This SOC is used to evaluate and to remove unwanted numerical error terms in the 3-D MoM simulation. Following a brief description on our SOC and 3-D MoM, a variety of uniplanar and multilayer circuits and antennas are characterized as their field/circuit models that involve all physical effects. SOC-extracted circuit parameters are obtained for uniplanar FGCPW structures, microstrip-fed slot radiator and layer-to-layer transition that are also well verified by our measurements. The models developed in this work are useful not only in gaining physical insight into their electrical behavior and also in applying efficient circuit network-oriented optimization approaches.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
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 head0.052
GPT teacher head0.230
Teacher spread0.178 · 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 designBench or experimental
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

Citations1
Published2003
Admission routes1
Has abstractyes

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