MétaCan
Menu
Back to cohort
Record W1987732413 · doi:10.1109/tmtt.2014.2327205

Low-Temperature Superconducting DC-Contact RF MEMS Switch for Cryogenic Reconfigurable RF Front-Ends

2014· article· en· W1987732413 on OpenAlexaff
Sara S. Attar, Sormeh Setoodeh, Raafat R. Mansour, Deepnarayan Gupta

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNiobiumResonatorOptoelectronicsCapacitorMicroelectromechanical systemsSuperconductivityMicroelectronicsRadio frequencyInsertion lossElectrical engineeringVoltagePhysicsCondensed matter physicsEngineering

Abstract

fetched live from OpenAlex

A niobium-based superconducting dc-contact RF microelectromechanical systems switch is introduced and is thoroughly analyzed. The switch is amenable to integration with superconducting microelectronics technology. A comparison of the switch's RF performance at room and cryogenic temperatures indicates a significant improvement in the insertion loss of the switch when niobium is superconducting. The mechanical characteristic of the switch at extremely low temperature (4 K) is also investigated. The switch exhibits an increase of 18% on the actuation voltage as the temperature changes from 293 K (room temperature) to 4 K. A niobium superconducting tunable resonator is designed and tested, employing the proposed switch as the tuning element in the form of a switched capacitor bank monolithically integrated with the resonator. The measured results at 4 K clearly indicate the discrete resonance frequency states of the resonator. A three-pole tunable bandstop filter is designed with a center frequency of 1.2 GHz and dimensions of only 5 mm × 1.2 mm. The results demonstrate a tuning range of 12% while maintaining an excellent RF response of the filter.

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: Empirical
Teacher disagreement score0.350
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 integrity0.0000.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Microwave Theory and TechniquesSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207