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Record W2943368127 · doi:10.1109/jestpe.2019.2905112

Special Section on Resonant and Soft-Switching Techniques With Wide Bandgap Devices

2019· article· en· W2943368127 on OpenAlexaff
Dehong Xu, Gerry Moschopoulos

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceSilicon carbideWide-bandgap semiconductorGallium nitrideOptoelectronicsPower semiconductor deviceEMIConvertersSnubberRingingParasitic extractionElectronic engineeringElectrical engineeringVoltageElectromagnetic interferenceCapacitorEngineeringNanotechnology

Abstract

fetched live from OpenAlex

The development of wide bandgap (WBG) devices in recent years, such as silicon carbide (SiC) and gallium nitride (GaN) power devices, has resulted in power converters with power densities and efficiencies that are not possible with traditional silicon (Si) devices. While the fast switching speed of WBG devices enables higher converter efficiency and power density, a number of issues are created such as increased sensitivity to parasitics, EMI noise, high voltage and current overshoot and ringing, and heat centralization in semiconductor devices. Resonant and soft-switching techniques can be effective in resolving these issues and allow even higher power densities and efficiencies to be achieved.

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 score0.157
Threshold uncertainty score0.577

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 communication0.0000.000
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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