Gate driver IC for enhancement mode GaN power transistors with senseFET reverse conduction detection circuit
Bibliographic record
Abstract
Dead‐times are necessary in switching output stage to avoid shoot‐through current between the high side (HS) and the low side (LS) power transistors. However, excessively long dead‐times can lead to unwanted reverse conduction and power loss. Sensing the duration of reverse conductions are especially difficult for high‐voltage enhancement mode ( e ‐mode) gallium nitride (GaN) HEMTs due to their fast switching speed. High‐precision sensing circuits are required for dead‐time correction as the load current changes and to withstand large voltage swings. Traditional CMOS‐based sensing circuits (e.g. standard logic gates) are not suitable for GaN‐based converters as they can only handle limited voltage ranges. In addition, severe undershoots (up to −4 V) may damage the sensing circuit. Here, a gate driver IC for e ‐mode GaN power output stages capable of detecting the presence of reverse conduction with a best resolution of 0.66 ns, a dead‐time adjustment resolution of 0.33 ns, and with on‐chip closed‐loop control is presented. In addition, a novel reverse conduction sensing circuit that can accommodate the large voltage swings at the switching node (SW) is also described.
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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) | 0.001 | 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) | 0.015 | 0.003 |
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".