A low-volume multi-phase interleaved Dc-Dc converter for high step-down applications with auto-balancing of phase currents
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a multi-phase interleaved dc-dc converter, targeted for high-step down applications including voltage regulator modules (VRM) for microprocessors, where low volume and high efficiency are the key priorities. The introduced architecture, reducing the voltage swing at the switching node, along with doubling the effective switching frequency of the inductor current ripple, allows reducing the size of the inductors by up to 4 times, resulting in superior dynamic regulation, while maintaining high power processing efficiency of above 90%. Along with the interleaved 2-phase operation of the introduced converter, practical implementation details, including gate driver implementation, startup, and additional features such as automatic phase current balancing, are also addressed. Experimental verifications with a 12-to-1.2 V, 10 A, 250 kHz prototype show proper functionality of the introduced converter.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it