Fully Integrated Buck Converter With Fourth-Order Low-Pass Filter
Bibliographic record
Abstract
Fully integrated buck converters are typically operated with a second-order LC low-pass filter and a switching frequency beyond 100 MHz. The motivation for such design choices is to reduce the size of passive components in the LC low-pass filter required for small output voltage ripple. However, in a buck converter with on-chip planar spiral inductors, a fourth-order filter can deliver better performance characteristics without area penalty. This paper presents a comparative study of a fourth-order LC low-pass filter versus a second-order LC low-pass filter with on-chip planar spiral inductors and on-chip capacitors. A fully integrated buck converter is then designed with a quasi-V2controller to demonstrate the benefits of a fourth-order LC low-pass filter. The prototype chip, which is implemented in a 65-nm CMOS process, produces a nominal voltage of 0.7 V from a 1-V supply. The fourth-order LC low-pass filter uses a total inductance of 1.8 nH and a total capacitance of 4 nF. Measurement results demonstrate fast transient response on the order of nanoseconds. A peak efficiency of 76.1% is achieved, and the output voltage ripple is kept below 15 mV over the entire range of load current from 40 to 180 mA.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".