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SiC Power Devices: Product Improvement Using Diffusion Soldering

2009· article· en· W1994261049 on OpenAlexaff
Matthias Holz, J. Hilsenbeck, Ralf Otremba, Alexander Heinrich, P. Türkes, Roland Rupp

Bibliographic record

VenueMaterials science forum · 2009
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceSolderingThermal conductivityStack (abstract data type)Engineering physicsOptoelectronicsPower semiconductor deviceDiodeReliability (semiconductor)VoltagePower (physics)Electrical engineeringComposite materialComputer scienceEngineering

Abstract

fetched live from OpenAlex

SiC power devices have reached a high market penetration, especially for high-voltage applications like switch mode power supplies. In the past, however, the superior material properties like, e.g., good thermal conductivity, have often not been put to full use due to the limitations of current packaging techniques. Especially the inferior thermal conductivity of current die attach materials have been an obstacle to realise the full potential of SiC technologies. In this paper, we describe in detail the use of diffusion solder for the die attach of SiC chips. Replacing the conventional solder layer by a thin metal stack for diffusion soldering, the thermal conductivity of the device is significantly improved. In addition, we show the positive impact of diffusion soldering on the assembly process and on the device reliability. These results are interesting for, both, SiC diodes and switches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.217
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2009
Admission routes1
Has abstractyes

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