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Record W2132377535 · doi:10.1017/s175907871000053x

New class of SiGe reduced-size loaded slow wave 60 GHz CPW series/shunt stubs and its applications to the design of slow wave bandpass filters

2010· article· en· W2132377535 on OpenAlexaff
Khelifa Hettak, G.A. Morin, M.G. Stubbs

Bibliographic record

VenueInternational Journal of Microwave and Wireless Technologies · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsDepartment of National DefenceDefence Research and Development CanadaCommunications Research Centre Canada
Fundersnot available
KeywordsCoplanar waveguideBand-pass filterShunt (medical)Capacitive sensingCapacitorElectronic circuitReturn lossElectronic engineeringInductorConductorComputer scienceMaterials scienceElectrical engineeringTopology (electrical circuits)OptoelectronicsMicrowaveEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This paper proposes a practical approach for developing a new class of compact slow-wave coplanar waveguide (CPW) series/shunt stubs, which offer 40% reduction in size relative to a conventional design. It demonstrates that the technique using interdigitated capacitive loading can provide size and cost reductions, while also providing performance enhancements such as better return loss. The experimental prototypes presented in this paper demonstrate the validity of the design method and the ability to print interdigitated capacitors inside the center conductor of the series/shunt stubs. The principle of achieving such high-quality circuits is detailed and is also confirmed by theoretical and experimental results, which are in reasonable agreement up to at least 70 GHz. The paper also presents a family of novel topologies of slow wave bandpass filters based on the proposed capacitively loaded CPW series/shunt stubs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.730

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 science0.0010.000
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 head0.014
GPT teacher head0.224
Teacher spread0.209 · 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 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
Published2010
Admission routes1
Has abstractyes

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