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Record W2971515186 · doi:10.1109/tpel.2019.2938827

A Dual-Switch Discontinuous Current-Source Gate Driver Overcoming the Current Diversion Problem for a Buck VRM

2019· article· en· W2971515186 on OpenAlexaff
Iman Abdali Mashhadi, Majid Pahlevani

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGate driverMOSFETPower MOSFETElectrical engineeringLogic gateCurrent (fluid)Driver circuitNAND gateVoltagePower (physics)EngineeringElectronic engineeringComputer scienceTransistorPhysics

Abstract

fetched live from OpenAlex

In this article, a novel dual-switch discontinuous current-source gate driver (CSD) suitable for driving the high-side mosfet (HS mosfet) of buck voltage regulator module (VRM) is presented. The proposed gate driver completely solves the gate current diversion problem, which most of the previous CSDs suffer from, during turn-off transition. Thus, in comparison to most of the previous CSDs, the proposed gate driver achieves to turn-off the power mosfet considerably faster and with much higher effective gate current, which leads to significant reduction of turn-off losses. Whereas, turn-on losses of buck VRM HS mosfet driven by the proposed CSD and the previous CSDs are equal. Furthermore, the introduced gate driver consists of the minimum number of control switches and circuit elements, compared to previous CSDs. The proposed gate driver is analyzed and a prototype of the driver operating at 1 MHz is implemented in order to validate the theoretical analysis.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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