Single Discharge Control for Single-Inductor Multiple-Output DC–DC Buck Converters
Bibliographic record
Abstract
With ever-increasing demand of feature-rich portable electronics, single-inductor multiple-output (SIMO) converters are becoming a cost-effective option to provide different supply voltages while maintaining a long battery life. A single-discharge (SDC) control scheme is proposed to simplify the SIMO design, to achieve low cross-regulation, and to support wide range of loads with reasonable power efficiency. A SDC SIMO buck converter with four outputs composed of comparators, phased-locked loop, finite state machine (FSM) controller, and an output stage, is prototyped using TSMC's 0.18-μm BCD process. In addition to the basic switching functions, the FSM controller provides a state skipping feature to allow no-load regulation. Its functionality is verified experimentally and can convert an input voltage from 2.7 to 3.7 V to 1, 1.2, 1.5, and 1.8 V outputs. The simulated peak power conversion efficiency is 86% with a combined output power ranging from 150 to 300 mW. The packaged SDC SIMO converter ICs exhibit a lower measured peak efficiency of 73%, with a cross-regulation of 0.24 mV/mA due to the parasitic bond-wire resistance. The converter prototype can support a wide range of loading conditions, from open circuit to a total output power of 1 W.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".