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Optimizing Permittivity Grading in Power Semiconductor Modules to Reduce Electric Field Stress at the Triple Junction

2025· article· en· W4413442541 on OpenAlexaff
Muneaki Kurimoto, Shesha Jayaram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPermittivityTriple junctionMaterials scienceElectric fieldOptoelectronicsSemiconductorGrading (engineering)Stress (linguistics)Electrical engineeringElectronic engineeringEngineering physicsComputer scienceEngineeringDielectricPhysics

Abstract

fetched live from OpenAlex

One of the major concerns in high-voltage power semiconductor modules is the high electric field stress at the edge of the metallization, where the substrate, encapsulation material, and electrode meet; commonly referred to as the triple junction. If this stress is not controlled, partial discharge may occur, eventually leading to insulation failure. To mitigate this issue, electric field grading techniques, such as the use of permittivity-graded materials in encapsulation, have been investigated. However, the optimal distribution of permittivity remains unclear. This study explores the effective distribution of relative permittivity (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon_{\mathrm{r}}$</tex>) in permittivity-graded materials for reducing electric field stress at the triple junction. By comparing the stress-reducing effects of four different types of permittivity distributions, the study aims to identify and discuss the most effective approach.

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 score0.008
Threshold uncertainty score0.442

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)0.0000.000

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 head0.242
Teacher spread0.230 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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