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Record W1510998189 · doi:10.1109/gsmm.2015.7175444

Air-filled SIW transmission line and phase shifter for high-performance and low-cost U-Band integrated circuits and systems

2015· article· en· W1510998189 on OpenAlexaff
Frédéric Parment, Anthony Ghiotto, Tan‐Phu Vuong, Jean‐Marc Duchamp, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsPhase shift moduleInsertion lossElectronic circuitMaterials scienceTransmission linePrinted circuit boardExtremely high frequencyTransmission lossOptoelectronicsKu bandSubstrate (aquarium)Monolithic microwave integrated circuitDielectric lossWaveguideElectrical engineeringElectronic engineeringDielectricTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, air-filled Substrate Integrated Waveguide (SIW) is proposed and demonstrated for the first time at U-band. This low-loss transmission line is developed on a low-cost multilayer Printed Circuit Board (PCB) process. The top and bottom layers may make use of an extremely low-cost standard substrate such as FR-4 on which base-band or digital circuits can be designed so to obtain a very compact, high performance, low-cost and self-packaged integrated system. For measurement purposes, an optimized-length dielectric- to air-filled SIW transition operating at U-band with 0.21 ±0.055 dB insertion loss is developed. The measured insertion loss of an air-filled SIW of interest at U-band is 0.122 ±0.122 dB/cm compared to 0.4 ±0.13 dB/cm for its dielectric-filled counterpart. Furthermore, an air-filled SIW phase shifter is reported for the first time. It achieves a measured 0.15 ±0.14 dB transmission loss at U-band. The proposed air-filled SIW transmission line and phase shifter are of particular interest for high performance and low-cost millimeter-wave circuits and systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.646

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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