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Record W2001994255 · doi:10.1109/tmtt.2009.2033850

Monolithically Integrated Multiport RF MEMS Switch Matrices

2009· article· en· W2001994255 on OpenAlexaff
Arash A. Fomani, Raafat R. Mansour

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInsertion lossMaterials scienceRF switchRadio frequencyCoplanar waveguideMicroelectromechanical systemsElectrical engineeringOptoelectronicsTransmission lineElectric power transmissionWaferMicrowaveComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The design methodology and performance of a miniature-size monolithically integrated RF microelectro-mechanical systems switch matrix is reported. The switch matrix has the form a of cross-bar configuration that can be easily expanded to realize a large size switch matrix. Three single-pole single-throw RF switches coupled to coplanar waveguide transmission lines are employed to construct the unit cell with dimensions of only 320 × 320 ¿m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The compact design of the proposed cell permits the high frequency operation of large switching networks. A six-mask fabrication process has been developed to construct the entire structure on a single side of the wafer. The impact of bias line resistance on the RF performance and the switching speed of the devices were studied. An excellent RF performance is achieved for a fabricated 4×4 switch matrix using high-resistive phosphorous-doped hydrogenated amorphous silicon semiconductor as the material of choice for the biasing lines. Over a frequency range from DC to 40 GHz, the worstcase measured results obtained for the insertion loss, return loss, and isolation are -1.8, -17, and 26 dB, respectively. A wide-band operation is predicted for an 8 × 8 switch matrix version constructed from 64 switching units.

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: none
Teacher disagreement score0.635
Threshold uncertainty score0.852

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 integrity0.0000.001
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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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