Optimizing PWM Control for Efficiency and Reduction of False Turn-On Events in Synchronous Buck GaN Converters
Bibliographic record
Abstract
Half-bridge GaN power converters are susceptible to false turn-on events, which can lead to shoot-through and potentially device-damaging currents. There are three main parameters that can be adjusted in PWM schemes to reduce the likelihood of false turn-on events: negative gate bias, gate resistance, and deadtime. However, these PWM parameters also affect converter efficiency in the inverse way, meaning less false turn-on events must be balanced with lowered efficiency. The novelty of this paper is to investigate the trade-off between reducing GaN false turn-on events (by reducing the transient peak of gate to source voltage) and maximizing the power converter efficiency, which has not been done in prior work. This paper investigates this trade-off using a synchronous buck converter over numerous operating points with variation of the three key PWM parameters. Six converter scenarios are considered with input voltage of 200/400V, switching frequency of 50/100kHz, and output power of 500W/1kW. For each scenario, negative gate bias is set to ™4.4V and −5V, gate on-resistance is set to$10\Omega $and$12.5\Omega $, and deadtime is varied at 60ns, 80ns, and 110ns. The results are organized into Pareto plots to find optimal points for efficiency and reduction of false turn-on events. The experimental results show that a further negative gate bias (−5V) most significantly reduces the false turn-on voltage peak and still achieves very high efficiency with appropriate selection of gate resistance and deadtime.
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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 | 0.001 |
| 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 | 0.001 | 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".