An Integrated High-Density Power Management Solution for Portable Applications Based on a Multioutput Switched-Capacitor Circuit
Bibliographic record
Abstract
This paper introduces a novel power management architecture (PMA) and its on-chip implementation, designed for battery-powered portable applications. Compared to the conventional two-stage PMA, consisting of a front-end inductive converter followed by a set of point-of-load buck converters, the presented PMA has drastically improved power density. The new architecture, named multioutput switched-capacitor convertor-differential-input buck (MSC-DB) convertor, is based on a novel hybrid converter topology that combines a fixed ratio MSC and a set of DB converters, to achieve low volume and high power processing efficiency. The front-end switched-capacitor stage has a higher power density than the conventionally used inductive converters. The downstream DB converters enable tight output voltage regulation, and allow for up to four times reduction of output filter inductors without the need for increasing switching frequency, hence limiting switching losses and improving the efficiency of the system. Furthermore, the new PMA is able to balance the state-of-charge of the input battery cells, a feature not existing in conventional systems. The PMA architecture is implemented both as a discrete prototype and as an application-specific integrated circuit (IC) module. The on-chip implemented architecture is fabricated in a standard 0.13-μm CMOS process and operates at 9.3-MHz switching frequency. Experimental comparisons with a conventional two-cell battery input architecture, providing 15 W of total power in three different voltage outputs, demonstrate up to two times reduction in the inductances of the downstream converter stages and more than two times reduction in losses, equivalent to the improvement of the power processing efficiency of a 12%. Moreover, the fabricated IC module is copackaged with low-profile thin-film inductors to demonstrate the effectiveness of the introduced architecture in reducing the volume of PMAs for portable applications.
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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 | 0.001 |
| 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".