Low-volume buck converter with adaptive inductor core biasing
Bibliographic record
Abstract
This paper introduces a digitally-controlled buck converter with adaptive core biasing that allows for minimization of the output capacitor as well as of the inductor core. The improved performances are obtained through adaptive relocation of the converter operating point on the B-H curve of the inductor core, with the help of a digitally controlled low-power biasing circuit and an extra inductor winding. During transients, the point is set in the saturation region, so the inductance is drastically reduced. As a result the inductor current slew rate and, consequently, load transient response are improved allowing output capacitor reduction. The biasing is also used to reduce flux density allowing the core volume minimization. Experimental verifications with a 3.3 V, 30 W, 500 kHz prototype show that the adaptive biasing system has about two times smaller voltage deviation than the conventional buck allowing for proportional reduction in the output capacitance and for about 40% reduction in the magnetic core size.
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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".