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Record W3197002767 · doi:10.1002/cta.3136

Digital active gate drive of SiC MOSFETs for controlling switching behavior—Preparation toward universal digitization of power switching

2021· article· en· W3197002767 on OpenAlexfundno aff
Hajime Takayama, Takafumi Okuda, Takashi Hikihara

Bibliographic record

VenueInternational Journal of Circuit Theory and Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
FundersProgram on Open Innovation Platform with Enterprises, Research Institute and AcademiaJapan Society for the Promotion of ScienceSwine Innovation Porc
KeywordsGate driverRingingElectrical engineeringPower semiconductor devicePower MOSFETEMIGround bounceSwitching timeElectronic engineeringWaveformMOSFETNAND gatePower (physics)VoltageEngineeringLogic gateElectromagnetic interferenceGate oxideTransistorFilter (signal processing)Physics

Abstract

fetched live from OpenAlex

Summary In this paper, a digital active gate driver for SiC power MOSFETs is proposed. High‐frequency switching with SiC power MOSFETs can realize an integrated power circuit with higher power density. However, the large surge voltage and ringing caused by the fast switching will lose the reliability of the device and increase electromagnetic interference (EMI) problems. To achieve high‐frequency switching without these drawbacks, an active gate driver based on the architecture of a digital‐to‐analog converter has been designed. The gate‐source voltage waveform of the MOSFET is generated directly and flexibly by a multibit gate signal sequence, considering the device characteristics and a variety of circuit conditions. Effective gate signal sequences are investigated by focusing on the switching trajectory on the state space of the device, which is discretely controlled through successive transitions between operating points. Experimental and simulated results confirm that the proposed gate driver effectively suppresses and regulates the surge voltage and ringing during turn‐off.

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 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 score0.395
Threshold uncertainty score0.404

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 communication0.0000.001
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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