MétaCan
Menu
Back to cohort
Record W1580722841 · doi:10.23919/eumc.2011.6101871

A novel reconfigurable impedance matching network using tunable MEMS capacitive and inductive components

2011· article· en· W1580722841 on OpenAlexaff
Elhadji Mansour Fall, Frédéric Domingue, Siamak Fouladi, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of WaterlooUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsCapacitive sensingImpedance matchingSmith chartElectrical impedanceMaterials scienceTransmission lineMicroelectromechanical systemsElectronic engineeringElectrical engineeringRadio frequencyMulti-band deviceHigh impedanceOptoelectronicsEngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

A novel reconfigurable impedance matching network suitable for tunable RF front-ends is presented. The network is based on a transmission line loaded with 8 dual state MEMS capacitive and inductive devices and achieves 256 different impedance states. The application of both the capacitive and inductive elements results in a more uniformly distribution of the impedance states on the Smith chart compared to the conventional DMTL matching networks. The fabricated network on alumina substrate is compact (4.0 × 1.9 mm2) and demonstrates good performance in terms of impedance coverage for a frequency band from 8 GHz up to 14 GHz with a low insertion loss.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.218
Teacher spread0.144 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicRadio Frequency Integrated Circuit DesignFrench-language works237,207