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Record W2313296558 · doi:10.1109/tmtt.2014.2365539

Low Loss, Wideband, and Compact CPW-Based Phase Shifter for Millimeter-Wave Applications

2014· article· en· W2313296558 on OpenAlexaff
A. S. Abdellatif, MohammadSadegh Faraji-Dana, Nazy Ranjkesh, Aidin Taeb, Mehrdad Fahimnia, Suren Gigoyan, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhase shift moduleCoplanar waveguideWidebandExtremely high frequencyInsertion lossPhase (matter)DielectricPhysicsMonolithic microwave integrated circuitOptoelectronicsMaterials scienceOpticsMicrowaveComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a compact, low-loss, and low cost phase shifter for millimeter-wave phased array systems. The basic idea is to modify the propagation mode of a coplanar waveguide (CPW) by placing a high dielectric constant (40 <; ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> <; 170) slab on top of it. The phase shift is varied by changing the air gap between the CPW line and the dielectric slab. A piezoelectric transducer has been used to control this air gap precisely. For fast but accurate modeling of the proposed phase shifter, two methods one based on spectral domain analysis and the other based on the conformal mapping have been developed and verified with full wave simulations and measurements. A prototype structure with the operational frequency range from 20 to 40 GHz is presented. The maximum phase shift obtained for the electrically controlled version at 40 GHz is 103 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> with loss variation of 0.2 dB. The total length is 2 mm.

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: none
Teacher disagreement score0.942
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 head0.233
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.

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

Citations22
Published2014
Admission routes1
Has abstractyes

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