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 ($T_{j}$) for power modules with asymmetric substrates. Then, a stray inductance ($L_{s}$) 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$T_{j}$and$L_{s}$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$L_{s}$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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".