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Record W2562344022 · doi:10.1109/estc.2016.7764473

Design of a GaN HEMT based inverter leg power module for aeronautic applications

2016· preprint· en· W2562344022 on OpenAlexaff
Benoit Thollin, Fadi Zaki, Zoubir Khatir, Régis Meuret, Donatien Martineau, Clément Fita, Pierre‐Olivier Jeannin, Johan Delaine, Pierre Lefranc, Laurent Mendizabal, René Escoffier, Farid Hamrani, Laurent Quellec, Eric Lorin

Bibliographic record

Venue2016 6th Electronic System-Integration Technology Conference (ESTC) · 2016
Typepreprint
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsEMIDecoupling (probability)High-electron-mobility transistorInverterCapacitorComponent (thermodynamics)Electronic componentPower (physics)TransistorElectrical engineeringComputer scienceThermalPower moduleElectronic engineeringMaterials scienceEngineeringElectromagnetic interferenceControl engineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

This paper presents the design of an inverter leg power module using GaN HEMT power components, and dedicated to aeronautical transports domain. This module has been designed collectively between industrial and academic partners, considering the thermal, electrical and thermomechanical aspects while co-developing the component and the package. The environment of the transistors is especially adapted to GaN materials for high frequency and high temperature operating by, among other thing, adapting the power components surface and bringing closer the decoupling capacitors and the control system. The first part of the paper describes the original part of the module, the second one is focused on the electrical and EMI aspects, the third part presents thermal simulations and the last one describes the thermomechanical analysis of the structure.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.001
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 head0.019
GPT teacher head0.236
Teacher spread0.216 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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