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Record W2172139453 · doi:10.1109/tpel.2007.915769

A Current Source Gate Driver Achieving Switching Loss Savings and Gate Energy Recovery at 1-MHz

2008· article· en· W2172139453 on OpenAlexaff
Wilson Eberle, Zhiliang Zhang, Yan‐Fei Liu, P.C. Sen

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2008
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsInductorDuty cycleElectrical engineeringGate driverConstant currentPower (physics)Current (fluid)Electronic engineeringComputer scienceVoltageEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a new current source gate drive circuit is proposed for power MOSFETs. The proposed circuit achieves quick turn on and turn off transition times to reduce switching loss and conduction loss in power MOSFETs. In addition, it can recover a portion of the CV$^{2}$gate energy normally dissipated in a conventional driver. The circuit consists of four controlled switches and a small inductor (typically 100 nH or less). The current through the inductor is discontinuous in order to minimize circulating current conduction loss. This also allows the driver to operate effectively over a wide range of duty cycles with constant peak current—a significant advantage for many applications since turn on and turn off times do not vary with the operating point. Experimental results are presented for the proposed driver operating in a boost converter at 1 MHz, 5 V input, 10 V/5 A output. At 5 V gate drive, a 2.9% efficiency improvement is achieved representing a loss savings of 24.8% in comparison to a conventional driver.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations148
Published2008
Admission routes1
Has abstractyes

Explore more

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