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Record W4319068777 · doi:10.1109/tmag.2023.3242088

Inkjet-Printed Ferrite Substrate-Based Vialess Waveguide Phase Shifter

2023· article· en· W4319068777 on OpenAlexaff
Ulan Myrzakhan, Farhan A. Ghaffar, Mohammad Vaseem, Hossein Fariborzi, Atif Shamim

Bibliographic record

VenueIEEE Transactions on Magnetics · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsLakehead University
Fundersnot available
KeywordsPhase shift moduleFerrite (magnet)Materials scienceFabricationPrinted circuit boardOptoelectronicsWaveguideYttrium iron garnetSubstrate (aquarium)Electronic circuitInsertion lossElectrical engineeringOpticsPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Magnetically controlled waveguide-based phase shifters are desirable for their high performance, but are bulky and heavy, thus cannot be easily integrated with printed circuit board (PCB) based circuits. To tackle this, substrate-integrated waveguide (SIW) technology has been utilized, which brings the waveguide to a standard PCB, but requires large numbers of vias as well as multiple cavities in the substrate for ferrite slab placement. This implementation technique involves complex fabrication and yields a relatively low figure of merit (FoM). To alleviate this, we present the first completely vialess ferrite substrate-based waveguide phase shifter realized through low-cost inkjet printing technique. All the four sides of a yttrium iron garnet (YIG) substrate have been metalized through inkjet printing, allowing the fabrication of a conventional rectangular waveguide on a standard magnetic substrate. The prototype has been tested in symmetric as well as antisymmetric modes of biasing and peak FoMs of 160°/dB at 7.22 GHz and 332°/dB at 7.46 GHz have been measured, which are higher than those of the previously reported designs. This all-around inkjet printing approach can open the door to low-cost, integrable magnetic phase shifters with excellent RF performances.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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 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

Citations17
Published2023
Admission routes1
Has abstractyes

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