Analysis and Design of Multiphase, Reconfigurable Switched-Capacitor Converters
Bibliographic record
Abstract
A comprehensive framework for the analysis and design of multiphase, reconfigurable switched-capacitor (SC) dc-dc converters is presented. A generalized method, utilizing graph theory and network analysis, is first proposed, which enumerates all attainable ideal conversion ratios for a given SC converter structure. From this analysis, the performance limits of multiphase SC converters are expanded, and a reconfigurable, multiphase SC dc-dc converter is proposed. The converter utilizes a programmable and optimized switching configuration that can generate an increased number of attainable ideal conversion ratios for the given number of capacitors and switching states. This property allows the converter to maintain high efficiency across a wide range of operating conditions and achieve very high step-up and step-down conversion ratios. A set of sample switching sequences are provided to generate any attainable ideal conversion ratio, for up to four capacitors, using a minimal number of switching states. An experimental prototype of the converter has been designed and fabricated as an integrated circuit using 0.35-μm CMOS technology to validate the performance.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".