A Dynamic Gate Driver with Auto-Patterning to Reduce Ringing and Switching Loss
Bibliographic record
Abstract
Parasitics in the gate loop can cause undesirable ringing oscillation for SiC power MOSFETs. Normally, ringing can be suppressed by adding a fixed gate resistor$\left(R_{G}\right)$to slow down the switching transient by limiting the gate current ($I_{G}$). However, a large$\boldsymbol{R}_{\boldsymbol{G}}$will increase the turn-on or turn-off times and the switching loss. Dynamic gate driving is implemented to mitigate this issue by adjusting$R_{G}$during different phases of the gate transient. The optimal timing (topt) was traditionally determined through a trial-and-error procedure. In this paper, we proposed a practical dynamic gate driver IC that can automatically identify the appropriate$R_{G}$driving pattern to achieve fast switching, low ringing, and improved efficiency. The end of the Miller plateau is detected as the point to adjust the gate driving strength$\left(R_{G}\right)$. An internal switched capacitor filter is designed to sense the end of the Miller plateau by monitoring the gate node ($V_{\text {Gate }}$). This timing with respect to the PWM input signal, topt, is used to generate a new gate driving pattern for the next switching cycle. Tests were conducted using a double pulse test (DPT) for varying IDS conditions. A value of 507 ns for$t_{O P T}$is obtained for$I_{D S}=3 \mathrm{A}, V_{D S}=15 \mathrm{V}$, and$V_{\text {Drive }}=$15 V. Applying topt to the gate driving pattern on the following DPT cycle, ringing undershoot on$V_{\text {Gate }}$, switching speed, and switching loss are improved by$59 \%, 74 \%$, and 57 %, respectively, when compared to the fixed large$R_{G}$method.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".