Thermal–Electrical Modeling and Co-Optimization of a Half-Bridge Power Module With Silver- Sintered Molybdenum Packaging
Bibliographic record
Abstract
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%
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".