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Record W2014680148 · doi:10.1109/mwsym.2006.249466

Micromachined Millimeter-wave Ridge Waveguide Filter with Embedded MEMS Tuning Elements

2006· article· en· W2014680148 on OpenAlexaff
W. P. Yan, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSurface micromachiningMicroelectromechanical systemsExtremely high frequencyWaveguide filterMaterials scienceFilter (signal processing)RidgeActuatorFabricationWaveguideCoplanar waveguideMillimeterInsertion lossEtching (microfabrication)OptoelectronicsAcousticsElectronic engineeringPrototype filterOpticsComputer scienceFilter designEngineeringElectrical engineeringPhysicsMicrowaveTelecommunicationsNanotechnologyGeology

Abstract

fetched live from OpenAlex

This paper demonstrates the feasibility of constructing micromachined 3-dimensional ridge waveguide filters with built-in tuning elements. The objective of the tuning elements is to fine tune the filter in order to compensate for the fabrication tolerance of the micromachining process. A novel micromachined ridge waveguide filter with a coplanar line input/output interface is proposed. The structure is analyzed both theoretically and experimentally. A 2-pole micromachined millimeter-wave filter with embedded MEMS actuators as tuning elements is designed, fabricated and tested to demonstrate the concept. At 24 GHz, the filter size is only 2mm times 4mm

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 score0.306
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 head0.009
GPT teacher head0.191
Teacher spread0.181 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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