WBG Power Semiconductor Module Packaging Insulation Characteristics Under Electrothermal Stress
Bibliographic record
Abstract
As core components of increasing power density, the new generation wide bandgap (WBG) power semiconductor modules are advancing toward higher voltage levels. However, the reliability of packaging insulation serves as a significant constraint, notably concerning the insulation degradation induced by high temperatures working conditions within confined spaces. In this article, a simplified power module test model featuring a directed bonding copper (DBC) and silicone gel was established. Through partial discharge (PD) tests across temperatures spanning 25 °C–175 °C and different etching distances for substrate layout, the insulation degradation mechanism of power modules at varying temperatures is comprehensively explored. The results indicate an obvious reduction in the PD inception voltage (PDIV) with rising temperature, exhibiting an uptrend over 75 °C. Simultaneously, the discharge amplitude initially increases and subsequently decreases with rising temperature. The thermal expansion of tiny air-gap defects within the silicone gel emerges as a significant factor influencing the module’s insulation characteristics. Ultimately, by establishing a proportional air-gap defect model, the electric field and space charge distribution were studied under electrothermal coupling, validating the effectiveness of the insulation defect mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".