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Record W4401829283 · doi:10.4028/p-cchu90

Influence of Material Properties on Ruggedness Evaluation of Package Architectures for SiC Power Devices

2024· article· en· W4401829283 on OpenAlexaff
Hans-Juergen Funke, Zhihong Li, Haibo Fan, Ou Dong, April Joy Garete, Tom Birkoben, Christian Liguda, S. Habenicht

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2024
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMaterials scienceMoldComposite materialStack (abstract data type)Delamination (geology)Die (integrated circuit)AdhesionStress (linguistics)Layer (electronics)NanotechnologyComputer science

Abstract

fetched live from OpenAlex

Button shear tests at different temperatures between different mold compounds and Cu-leadframes have been performed to evaluate the adhesion of mold compounds to the inner surfaces of SiC-power devices. The results at different temperatures show the behavior of different material and layer stack combinations under storage at room temperatures as well as under operating conditions with elevated temperatures, where adhesion and thus the hermeticity of the device against moisture is significantly lowered. It has been shown that different thermomechanical properties of the mold compounds as well as the usage of different adhesion promoter materials have a significant effect on the adhesion properties of the mold compound to the leadframe surface of the SiC power devices, which have direct impact on the ruggedness and lifetime stability. Furthermore, these results have been correlated to thermomechanical simulations of the package architecture of SiC power devices under stress. Different wire bond architectures have been evaluated in simulations with and without die top delamination taken into account, showing that EMC delamination may play a major role in the lifetime stability of SiC power devices under thermomechanical stress like simulated in TCT- , IOL- and PTC-testing.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.307
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.052
GPT teacher head0.309
Teacher spread0.257 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207