A Reconfigurable Resonant Modular Multilevel PMIC for Wide Input Voltage Range With Inherent Balancing and Soft Switching in 180 nm CMOS
Bibliographic record
Abstract
Design of integrated power converters capable of operating with a wide input voltage range in standard CMOS technologies is challenging due to the low breakdown voltage of modern transistors. This paper presents a fully integrated, fully soft-switched resonant modular multilevel converter (IRMMC), designed in standard 180 nm CMOS with nominal supply voltage of 1.8 V, that enables high-efficiency power conversion over a wide input voltage range of 2 V to 5.5 V—well beyond the safe operating limits of scaled CMOS devices. The proposed architecture combines multilevel voltage division, resonant energy conversion, and modular reconfiguration to simultaneously support wide input adaptability, inherent flying-capacitor voltage balancing, and continuous output voltage regulation. A dynamically reconfigurable multilevel converter, utilizing an additional sub-module and control logic, enables seamless mode transitions and capacitor self-balancing without the need for complex sensing or control loops. An integrated LLC resonant tank, using a 0.8 nH in-package series inductor, along with an active rectifier, achieves output regulation from 0.4 V to 1.2 V and ensures full zero-voltage switching (ZVS) for all power MOSFETs across the entire operating range. The prototype converter delivers a maximum output power of 2 W with a peak efficiency of 88%, demonstrating its suitability for compact, energy-efficient power delivery in integrated system-on-chip (SoC) 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 | 0.000 |
| 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".