The Current Sharing Optimization of Paralleled IGBTs in a Power Module Tile Using a PSpice Frequency Dependent Impedance Model
Bibliographic record
Abstract
A full circuit model of an insulated gate bipolar transistor (IGBT) power module tile is obtained using electro-magnetic analysis. A skin depth analysis on the copper and bonding wire interconnects shows that the commonly used low frequency RLC models are not adequate for power modules and reveals the necessity for a frequency dependent impedance. A method is adapted for the design of an f-dependent module tile design circuit for the first time. It is found that the importance of skin depth becomes vital especially for lower voltage ratings and faster edge rate devices as the Fourier transform becomes shifted towards higher frequencies. Analysis of the current sharing capability of the IGBTs as a function of the tile layout showed that an asymmetrical tile yields a faster response from the nearer IGBTs during inductive turn-off and an unbalanced sharing of the reverse recovery current during turn-on. A symmetric tile layout is developed. It is found that the faster current fall-time during turn-off is compensated by higher current tail oscillations thereby bringing no major improvement to the turn-off energy. During turn-on however, the reverse recovery and IGBT turn-on energy dissipation are equally distributed between the IGBTs. Simulating device imbalances also shows that threshold voltage variations have a significant effect on the current and energy sharing at turn-on whereas injection efficiency imbalances have a large influence on turn-off characteristics. The effect of device imbalances is found to decrease for a highly symmetric tile design. It is also shown that equally higher IGBT temperatures tend to improve current sharing so that IGBT module design emphasis should be placed rather on minimizing any differences in the IGBT junction temperatures. The model developed allows PSpice simulation of characteristics previously obtained only through measurements. The impact of the layout design on issues such as cross-talk, mutual inductance and ground bounce may be quickly and accurately assessed for an optimal current sharing capability.
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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 | 0.001 |
| 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) | 0.002 | 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".