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Record W2893722414 · doi:10.1109/tmtt.2018.2868941

Nonuniformly Distributed Electronic Impedance Synthesizer

2018· article· en· W2893722414 on OpenAlexaff
Yangping Zhao, Simon Hemour, Taijun Liu, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsImpedance matchingElectronic engineeringElectrical impedanceSmith chartFigure of meritParticle swarm optimizationComputer scienceMatching (statistics)Output impedanceEngineeringElectrical engineeringAlgorithmMathematics

Abstract

fetched live from OpenAlex

Benefiting from advantageous features of fast tuning, small size, and easy integration, the electronic impedance synthesizer (EIS) has been developed for on-wafer load-pull characterization systems, tunable matching networks, reconfigurable devices and systems, and so on. The previous designs of the EIS were mostly based on the empirical data instead of a closed-form design. Moreover, incomplete figures of merit (FOM) were chosen to optimize and evaluate the EIS. In this paper, we propose and present, first of all, a semiclosed-form design procedure for the distributed EIS. Then, a particle swarm optimization method is introduced to optimize the proposed nonuniformly distributed EIS, which comprises an adjusting circuit and a nonuniformly distributed circuit. Experimental results demonstrate that the proposed nonuniformly distributed structure can not only improve the Smith chart coverage but also reduce the size, compared to the uniform counterpart. The fabricated nonuniform EIS operating from 0.8 to 2.5 GHz, exhibits a good agreement between theory and measurement. Furthermore, the most comprehensive FOM are presented to evaluate the fabricated EIS.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.705

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 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

Citations24
Published2018
Admission routes1
Has abstractyes

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