A Spurious-Free Switching Buck Converter Achieving Enhanced Light-Load Efficiency by Using a $\Delta \Sigma$-Modulator Controller With a Scalable Sampling Frequency
Bibliographic record
Abstract
This paper presents a spurious-free switching buck converter with enhanced light-load efficiency for use in noise-sensitive portable electronics. The proposed switching buck converter achieves low output noise by using a delta-sigma-modulator (ΔΣ) controller. Its light-load efficiency is enhanced by: 1) scaling the switching frequency of the buck converter (i.e., the sampling frequency of its ΔΣ-modulator controller) with the load current; 2) switching its operation from continuous conduction mode (CCM) to discontinuous conduction mode (DCM) at light loads; and 3) using a new low-power current-sensing circuit. The ΔΣ modulator is designed with an input-feedforward architecture, which enables the switching frequency of the controller to be scaled without disturbing the stability of the feedback loop of the buck converter, and also reduces the controller quiescent current. The proposed switching buck converter was fabricated in 0.13-μm digital CMOS. Measurements results demonstrate that this buck converter achieves a spurious-free output with a noise floor below -60 dBm and voltage ripples below 70 mV over its full loading range (2 mA to 800 mA). Furthermore, it achieves a power efficiency higher than 70% over this entire range, with a peak efficiency of 95.1%.
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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) | 0.001 | 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".