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Record W2035609432 · doi:10.1049/iet-map.2013.0251

Broadband coplanar‐waveguide and microstrip low‐noise amplifier hybrid integrations for K‐band substrate integrated waveguide applications on low‐permittivity substrate

2014· article· en· W2035609432 on OpenAlexafffund
Farzaneh Taringou, Jens Børnemann, Ke Wu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique MontréalUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMicrostripBroadbandCoplanar waveguideMaterials scienceSubstrate (aquarium)OptoelectronicsPermittivityWaveguideAmplifierNoise (video)Electronic engineeringTelecommunicationsOpticsPhysicsEngineeringComputer scienceMicrowaveDielectric

Abstract

fetched live from OpenAlex

K‐band low‐noise amplifier (LNA) chips are integrated in low‐permittivity coplanar waveguide (CPW) and microstrip (MS) structures and are interconnected with substrate integrated waveguide (SIW) ports for direct applications in hybrid SIW technology. A commercial Hittite LNA chip is employed, and special transitions provide the interface between CPW or MS and SIW. The LNA with SIW‐to‐CPW transitions achieves more than 20 dB gain between 18 and 26.5 GHz, and input/output return losses remain below 10 dB between 21.5 and 26.5 GHz. Related design values for the LNA integration within SIW‐to‐MS transitions are: the gain is greater than 21 dB; the input/output return loss is better than 12 dB over the entire frequency range. Measurements of a back‐to‐back SIW‐to‐CPW transition are provided to put these values in perspective. The maximum noise figures are measured to be better than 4.6 and 4.2 dB, respectively, between 18 and 26.5 GHz. A comparison with measurements performed with an evaluation board supplied by the LNA manufacturer demonstrates successful K‐band LNA chip integrations within CPW/MS and SIW.

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)0.001
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 score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.214
Teacher spread0.205 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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