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Record W2903964140 · doi:10.1109/icsict.2018.8564869

Design Trends in Smart Gate Driver ICs for Power GaN HEMTs

2018· article· en· W2903964140 on OpenAlexaff
Wai Tung Ng, Jingshu Yu, Mengqi Wang, Rophina Li, Weijia Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGallium nitridePower semiconductor deviceTransistorMaterials scienceGate driverPower electronicsWide-bandgap semiconductorLogic gatePower (physics)OptoelectronicsElectrical engineeringElectronic engineeringEngineeringNanotechnologyVoltage

Abstract

fetched live from OpenAlex

Gallium nitride (GaN) power transistors are gaining rapid acceptance by the power electronics industry as the next generation wide bandgap (WBG) power semiconductor technology. Early direct drop-in replacement attempts of the silicon based power MOSFETs with GaN power devices either produce no significant performance improvement or even degradation in some situations. This is mainly due to the fact that the GaN power devices were not driven properly to fully exploit their true performance. In this paper, different gate driving techniques for both depletion and enhancement mode GaN power transistors are examined. In addition, the recent trends on integrated GaN pre-driver and GaN power ICs is also discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.083
Threshold uncertainty score0.995

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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