Impact of PCB Parasitic Capacitance on Switching Transients in Chopper and Half-Bridge Configurations Utilizing TO-247 SiC Devices
Bibliographic record
Abstract
Silicon Carbide (SiC) MOSFETs and Schottky diodes in the TO-247 package are economical options for chopper (buck/boost) and half-bridge configurations, which are fundamental building blocks for various power converter topologies. However, the fast switching of SiC implies high$\text{d}\boldsymbol{v}/\text{d}\boldsymbol{t}$and$\text{d}\boldsymbol{i}/\text{d}\boldsymbol{t}$, imposing a constraint on the PCB portion of power loop inductance in minimizing voltage overshoot during the turn-OFF transient. Although the vertical PCB power loop layout effectively reduces the PCB loop inductance, it increases the PCB parasitic capacitance. Due to the considerable lead inductance of the TO-247 package, this PCB capacitance is paralleled to the device's output capacitance through the package lead inductance, altering the switching transient. This article analyzes the effect of PCB capacitance on turn-OFF switching transient and ringing in chopper and half-bridge configurations with SiC devices in the TO-247 package. Initially, small-signal models incorporating PCB capacitance are derived. Subsequently, these models are validated in the frequency domain, and the switching transients are compared through double pulse test (DPT) on two PCB prototypes with the same layout but different stack-ups, yielding different PCB capacitances. Further, a comparative study of the proposed models with direct parallel approximation of PCB and device output capacitance is presented. Finally, the proposed small-signal models are analyzed to establish criteria, in terms of TO-247 lead and PCB loop inductance, for minimizing the impact of PCB capacitance on switching transients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".