MétaCan
Menu
Back to cohort
Record W4381299402 · doi:10.1109/tpel.2023.3281417

Thermal–Electrical Modeling and Co-Optimization of a Half-Bridge Power Module With Silver- Sintered Molybdenum Packaging

2023· article· en· W4381299402 on OpenAlexafffund
Yuhang Yang, Linke Zhou, Omar Zayed, Maryam Alizadeh, D. V. Stevanovic, Mehdi Narimani, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNotationBridge (graph theory)AlgorithmParticle swarm optimizationMathematicsTopology (electrical circuits)CombinatoricsArithmetic

Abstract

fetched live from OpenAlex

This article proposes a methodology of analytical modeling and optimization of power modules, especially compatible with modules with silver-sintered molybdenum (SSM) packaging or other insulated metal substrate types of packaging schemes. First, a decoupled Fourier-based thermal model is presented, which considers the barrier effect between substrate segments. Compared with the original Fourier-based model, it reduces the average error from 93.8% to 10.9%, when estimating the difference of junction temperatures ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{j}$</tex-math></inline-formula> ) for power modules with asymmetric substrates. Then, a stray inductance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{s}$</tex-math></inline-formula> ) model is developed based on the partial inductance method and the actual current distribution, whose error is less than 12.1% when tested with example half-bridge SSM modules. Next, analytical models are combined with the particle swarm optimization algorithm to design a half-bridge power module with SSM packaging. Numerical simulations prove that the analytical estimations of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{j}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{s}$</tex-math></inline-formula> of the optimized module are accurate, with errors of 4.6% and 8.3%, respectively. The fabrication process of the designed SSM module is then elaborated. Finally, the accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{s}$</tex-math></inline-formula> estimation is validated by the double-pulse test, where the error is 0.4%. The junction-to-case thermal resistance is characterized by the structural function analysis, in which the error is 3.4%

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207