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Record W1598644662 · doi:10.23919/eumc.2011.6101709

Novel millimeter-wave slow-wave phase shifter using MEMS technology

2011· article· en· W1598644662 on OpenAlexaff
Maher Bakri-Kassem, Raafat R. Mansour, Safieddin Safavi‐Naeini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhase shift moduleCapacitive sensingInsertion lossInductorExtremely high frequencyMaterials scienceMicroelectromechanical systemsCapacitorElectrical engineeringCapacitive couplingSIGNAL (programming language)OptoelectronicsPhase (matter)Radio frequencyReturn lossCoupling (piping)VoltageEngineeringPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A novel mm-wave MEMS phase shifter based on a slow wave structure is introduced. The phase shifter uses co-planer wave guide that has the signal line loaded with inductors through capacitive coupling. The inductor and the capacitive coupling capacitor are designed to work for the mm-wave frequency range. Integrated metal to metal RF MEMS switches are designed such that inductors can be shortened to ground to obtain a normal CPW when the switches are closed. When the switches are open, the signal is traveling in the slow wave structures and a phase shift is obtained. Unlike other phase shifters, the proposed design can handle higher power and does not have neither MEMS capacitive nor metal to metal switches on the signal line. The measured pull-in voltage is 45 volt. The phase shifter is designed, modeled, fabricated and tested. The phase shift per length is 355 °/cm. The phase shifter exhibits high resolution of phase shift while maintaining a return loss of 20 dB and a worst insertion loss of 3.21 dB at 60 GHz.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.478
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.059
GPT teacher head0.232
Teacher spread0.174 · 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.

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

Citations15
Published2011
Admission routes1
Has abstractyes

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