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Record W4385834263 · doi:10.1109/temc.2023.3300988

A Coupled Transmission-Line-Based Measurement Technique for Currents in Switch-Mode Converters

2023· article· en· W4385834263 on OpenAlexafffund
Gabriel Nobert, Nicolas Constantin, Yves Blaquière

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2023
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResistive touchscreenDistortion (music)Transmission lineElectronic engineeringInductanceConvertersElectrical engineeringSystem of measurementElectrical impedanceMaterials scienceVoltageEngineeringPhysicsCMOS

Abstract

fetched live from OpenAlex

Power integrity issues arise at gigahertz (GHz)-range in compact systems integrating fast-switching power devices, such as gallium nitride high-electron mobility transistors. GHz-range current measurement techniques must be used in switch-mode converters to assess those issues. State-of-the-art techniques are insufficient due to the inherent limitations of the high-impedance probe in a pick-up coil or the impractical dimensions of the probing apparatus for current-surface probes. This article proposes a novel compact coupled transmission-line-based measurement technique that takes advantage of mutual inductance, allowing the measurement of currents in the GHz-range. A thorough analytical formulation shows that its measurement distortion can be predicted at up to multiple GHz and that the probing pad can be placed at any convenient location on the substrate without affecting the measurement distortion. Measurements with the proposed structure validated with a resistive shunt show that the current in a switch-mode converter can be characterized at up to 1.95 GHz with less than 3-dB measurement distortion, which represents more than 4-dB reduction in amplitude distortion with respect to a resistive shunt technique.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Electromagnetic CompatibilitySame topicElectromagnetic Compatibility and Noise SuppressionFrench-language works237,207