&#x210B;<sup>2</sup>-optimal thermal management for multi-phase current mode buck converters
Bibliographic record
Abstract
This paper demonstrates a new digital controller for thermal management in multi-phase current mode buck converters. While the majority of today's multi-phase designs emphasize equal load current sharing between all phases, variations in PCB layout, parasitic resistances, transistor on-resistance (Ron), and airflow, cause significant temperature variations between the converter phases. In this work, a digital multi-variable thermal management unit (TMU) based on ℋ2-optimization theory is demonstrated to rapidly achieve a uniform temperature distribution by adjusting the phase currents. Experimental results from a digitally controlled 12 V to 1 V, 50 A, 250 kHz four-phase peak current mode buck converter demonstrate a 5.1°C reduction in peak phase temperature and a 10.6°C reduction in phase temperature differences. This illustrates the effectiveness of the proposed thermal management technique in the presence of uneven air flow and multiple load steps. Infrared scans of the converter confirm that the peak and average temperatures are reduced, leading to improved long-term reliability. The TMU also exhibits stable transient response during load steps.
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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) | 0.004 | 0.001 |
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".