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A New Generation of SiC Schottky Diodes with Improved Thermal Management and Reduced Capacitive Losses

2010· article· en· W2006800925 on OpenAlexaff
Roland Rupp, Fanny Björk, Gerald Deboy, Matthias Holz, Michael Treu, J. Hilsenbeck, Ralf Otremba, Hannes Zeichen

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceSchottky diodeDiodeOptoelectronicsCapacitive sensingChipSilicon carbideSchottky barrierElectrical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

With the help of an improved die attach the Rth,jc of SiC Schottky diodes can be reduced by 40-50% at a given chip size. This enables a significant higher power density for these SiC diode chips, resulting in a chip shrink of ~ 35% for a given nominal current. This has a significant impact not only on the cost position of the device but also on the switching performance of the diodes, as their capacitive charge directly scales with the chip area. Of course these advantages are accompanied by a small penalty in static losses as the Vf of the diodes at nominal current also slightly increases by the chip shrink. However, the reduction of switching losses dominates upon the marginally increased static losses besides full load operation conditions (which are pretty exceptional in today’s SiC Schottky diode applications) combined with frequencies below 130 kHz. This allows a better competitive positioning against fast Si-based diodes and improved system efficiency at the same time.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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