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Record W1899399701 · doi:10.1109/aps.2003.1217479

Fabrication and modeling of an SP3T RF MEMS switch

2004· article· en· W1899399701 on OpenAlexaff
Mojgan Daneshmand, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCrossover switchMicroelectromechanical systemsCrossbar switchElectronic engineeringPort (circuit theory)Analogue switchComputer scienceChipWidebandElectrical engineeringEngineeringVoltageMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

Most of the research effort on RF MEMS switches reported in the literature has been directed toward the development of single-pole-single-throw (SPST) switches. The SPST switch is a two-port device, which acts as a simple RF relay. In today's communication systems, switches are typically used in the form of switch matrices. The use of multiport switches as the basic building blocks can considerably simplify the integration problem of large switch matrices. In our knowledge, very limited work has been reported on integrated multiport MEMS switches. We present a novel integrated SP3T MEMS switch. RF simulation, along with circuit modeling, is used to design the switch. Three beams with narrow-width tips are integrated on top of a coplanar transmission line. The junction where the three beams interact is inherently a wide band junction, which makes it possible to design a wideband SP3T switch with 30 dB isolation up to 20 GHz. Theoretical and measured results demonstrate the validity of the proposed design. Even though the concept is demonstrated using flip-chip technology, the SP3T switch can potentially be realized by integrating the beams and the substrate on one chip.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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 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

Citations15
Published2004
Admission routes1
Has abstractyes

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