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Record W2971939530 · doi:10.1109/mwsym.2019.8701106

Monolithically Integrated Reconfigurable RF MEMS Based Impedance Tuner on SOI Substrate

2019· article· en· W2971939530 on OpenAlexaff
Tejinder Singh, Navjot K. Khaira, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTunerSilicon on insulatorMicroelectromechanical systemsMaterials scienceSurface micromachiningCapacitive sensingCapacitanceInsertion lossOptoelectronicsCoplanar waveguideStictionFabricationImpedance matchingElectrical engineeringReturn lossElectrical impedanceRadio frequencySiliconEngineeringMicrowaveAntenna (radio)TelecommunicationsElectrodePhysics

Abstract

fetched live from OpenAlex

This paper presents the design and implementation of a MEMS-based impedance tuner realized on a Silicon-on-Insulator (SOI) substrate. Contactless lateral MEMS varactors were realized using laterally moving capacitive thick plates whose motion was precisely controlled using Chevron actuators. The voltage required for the maximum displacement is under 12 V. These varactors are monolithically integrated with CPW lines using a single mask fabrication process on SOI substrate. The implemented MEMS capacitive varactors exhibit a capacitance range of 0.19 pf to 0.8 pf. The improvement of the Smith chart coverage is achieved by proper choice of the electrical lengths of the CPW lines and precise control of the lateral motion of the capacitive plates. The measured results demonstrate a good impedance matching coverage with an insertion loss of 2.9 dB. Details of the SOI-based fabrication process are presented along with discussions on techniques to improve the insertion loss of the device. The proposed design does not suffer from the dielectric charging, micro-welding and stiction problems associated with RF MEMS devices realized using surface micromachining processes. In addition, the device promises to be useful in high power applications, since it is constructed from lateral thick structures.

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 score0.091
Threshold uncertainty score0.818

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)0.0010.001

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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