A 9.2-ns to 1-s Digitally Controlled Multituned Deadtime Optimization for Efficient GaN HEMT Power Converters<i/>
Bibliographic record
Abstract
This paper presents a tunable new deadtime control circuit providing an optimal delay for power converter optimization. Our method can reduce the deadtime loss while improving the efficiency and power density of a given power converter. The circuit presents a reconfigurable delay element to generate a wide range of deadtime for different power conversion applications with varying loads and input voltages. The optimal deadtime equation for buck converters is derived, and its dependency on the input voltage and load is discussed. Experimental results show that the presented circuit can provide a wide range of deadtime delays, ranging from 9.2 ns to 1000 ns. The power consumption of the presented circuit is measured for different capacitive loads ($\text{C}_{\mathrm {L}}$) and operating frequencies (${f}_{\mathrm {s}}$). The circuit consumed a power between 610$\mu \text{W}$and$850~\mu \text{W}$across the measured deadtime ranges while$\text{C}_{\mathrm {L}} =12$pF,$\text{V}_{\mathrm {dd}} =3.3$V, and$\text{f}_{\mathrm {s}}=200$kHz. The proposed deadtime generator can operate up to 18 MHz when the minimum deadtime of 9.2 ns is selected. The presented circuit occupies an area of$150\mu $m$\times 260\mu \text{m}$. The fabricated chip is connected to a buck converter to validate the operation of the proposed circuit. The efficiency of a typical buck converter with minimum$\text{T}_{\mathrm {DLH}}$and optimal$\text{T}_{\mathrm {DHL}}$at$\text{I}_{\mathrm {Load}} =25$mA is improved by 12% compared to a converter with a fixed deadtime of$\text{T}_{\mathrm {DLH}} =\,\,\text{T}_{\mathrm {DHL}} =12$ns.
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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) | 0.001 | 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".