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Record W2170956104 · doi:10.1109/tpel.2008.920877

A New Dual-Channel Resonant Gate Drive Circuit for Low Gate Drive Loss and Low Switching Loss

2008· article· en· W2170956104 on OpenAlexaff
Zhihua Yang, S. Ye, Yan‐Fei Liu

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsMOSFETGate driverElectrical engineeringGate equivalentTransistorRectifier (neural networks)Logic gateField-effect transistorPower MOSFETElectronic engineeringEngineeringGate oxideMaterials scienceComputer scienceVoltage

Abstract

fetched live from OpenAlex

At high-frequency applications, the gate drive loss of the power metal oxide semiconductor field-effect transistor (MOSFET) becomes quite significant. A new dual-channel low side resonant gate drive circuit is proposed in this paper. The proposed drive circuit can provide two symmetrical drive signals for driving two MOSFETs. It charges and discharges the MOSFET gate capacitor with a constant current source. Both gate drive loss and, more importantly, switching loss can be reduced significantly. The proposed resonant gate drive circuit can be used to drive the synchronous MOSFETs in a current doubler or full-wave rectifier configuration. It can also be used to drive the primary MOSFETs in push-pull converters. Analysis, computer simulation, and experimental results show that significant power loss reduction is achieved by the proposed circuit.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 head0.207
Teacher spread0.199 · 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

Citations71
Published2008
Admission routes1
Has abstractyes

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