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Record W4306778406 · doi:10.18280/ejee.240401

IGBT Wirebonds Ageing: A New Test Bench Development for Dedicated Modules Assemblies

2022· article· en· W4306778406 on OpenAlexvenueno aff
Guillaume Pellecuer, Jean-Jacques Huselstein, Thierry Martiré, François Forest, André Chrysochoos, Mourad Jebli

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
FundersUniversité de Montpellier
KeywordsTest benchAgeingProcess (computing)Automotive engineeringComputer scienceMechanical engineeringEngineeringEmbedded system

Abstract

fetched live from OpenAlex

Wirebonds ageing in power modules used in electric vehicles is a main concern for the development of reliable inverters. The development of a specific test bench to study the evolution of their properties over time has allowed conclusions to be drawn on the possibility of accelerating or not accelerating ageing tests without distorting the mechanisms actually involved in ageing. Through the use of specifically developed test bench in terms of electrical and thermal properties the studied wirebonds samples are more easily thermally controllable, equipped with measuring devices and the energy consumption at a given cycle is divided by ten compared to a classical bench. The entire process of designing and producing the specific samples and the associated test bench and a description of the first results obtained to demonstrate the relevance of the approach is given.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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