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Record W2777168866 · doi:10.1109/lmwc.2017.2779879

Negative Resistance-Based Electronic Impedance Tuner

2017· article· en· W2777168866 on OpenAlexaff
Yangping Zhao, Simon Hemour, T. Liu

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTunerImpedance matchingElectrical impedanceSmith chartElectrical engineeringDamping factorIMPATT diodeCharacteristic impedanceTransmission lineQuarter-wave impedance transformerInput impedanceOutput impedanceVaricapDiodeEngineeringElectronic engineeringPhysicsRadio frequencyCapacitance

Abstract

fetched live from OpenAlex

An electronic impedance tuner using the negative resistance of tunneling diodes is proposed in this paper. Aside from the fact that it is an interesting solution to synthesize impedance with reflection coefficient larger than one, this scheme is proven to be simpler and consume less power than the state-of-the-art techniques. The overall circuit topology consists of two parts, namely, impedance tuning circuit including a hybrid block of PIN and tunneling diode for generating a set of impedance points, and wideband nonlinear transmission line-based 360° phase shifter for rotating the set of impedance points around the Smith chart from 1.5 to 5 GHz. The operating power of the electronic tuner is below -25 dBm, which is limited by the tunneling diode. The worst-case maximum power consumption of the electronic tuner is as low as 3 mW. Such an electronic tuner should be useful for the development of on-wafer noise characterization 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 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 score0.011
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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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