Delay Mismatch Insensitive Dead Time Generator for High-Voltage Switched-Mode Power Amplifiers
Bibliographic record
Abstract
The Design of efficient, safe, and reliable circuits is a prime objective in high-voltage (HV) electronic systems, such as switched-mode power amplifiers (PAs). One of the main causes of efficiency degradation and reliability problems, in these amplifiers, is the shoot-through current from the HV power supply to the ground. To eliminate such current, a dead time generator (DTG) is used to modify the signals propagating through the high-side and low-side gate drivers by adding a fixed dead time between them. However, any delay mismatch between these gate drivers can reduce the dead time to the point that it becomes negative. In this paper, an HV-DTG architecture is introduced. The architecture mitigates the effects of delay mismatch variations in gate drivers, which can result from parameters mismatch, fabrication process variations, and temperature variations. An HV switched-mode class-D power amplifier is used to illustrate the performance of the DTG. The amplifier is implemented in a low-cost$0.35~\mu m$HV CMOS process. The total area of the PA is$0.5~mm^{2}$, where the DTG covers an area of$0.066~mm^{2}$. A measured system’s efficiency of 95.14% is achieved with the shortest dead time of 10.8 ns, which is 1.38x smaller than the generated dead time in comparable state-of-the-art HV dead time generators.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".