Active EMI Reduction Using Chaotic Modulation in a Buck Converter with Relaxed Output LC Filter
Bibliographic record
Abstract
DC-DC buck converters are widely used in portable applications because of their high power efficiency. However, their inherent fast switching releases electromagnetic emissions, making them prominent sources of electromagnetic interference (EMI). This paper proposes a voltage-controlled buck converter that reduces EMI by using a chaotic pulse-width modulation (PWM) technique based on a chaotic triangular ramp generator. The chaotic triangular ramp generator is constructed from a simple on-chip chaotic circuit linked with a symmetrically triangular ramp circuit. The proposed converter can thus operate in the chaotic mode reducing the EMI without requiring any EMI filters. Additionally, using the triangular ramp signal can relax the requirement for a large LC output filter in chaotic mode. The effectiveness of the proposed scheme was experimentally verified with a chaotic triangular ramp generator embedded in a voltage-mode controller buck converter using a 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) process. The measurement results from a prototype showed that the EMI improvement from the proposed scheme is approximately 14.53 dB at the fundamental switching frequency with respect to the standard fixed-frequency PWM reference case.
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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 | 0.000 | 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".