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Record W2136678985 · doi:10.1109/apec.2010.5433682

One-step digital dead-time correction for DC-DC converters

2010· article· en· W2136678985 on OpenAlexaff
Anyang Zhao, Arash A. Fomani, Wai Tung Ng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConvertersDead timeComputer scienceDigital controlElectronic engineeringMOSFETPower (physics)Buck converterPower MOSFETController (irrigation)VoltageControl theory (sociology)EngineeringElectrical engineeringControl (management)TransistorPhysics

Abstract

fetched live from OpenAlex

This paper introduces a novel one-step digital control technique that can dynamically optimize the dead-times for the turn-on and turn-off of the power MOSFETs in DC-DC converters. A NOR gate and a delay-line circuit are used to detect and measure the duration of the unwanted low-side MOSFET body-diode conduction. Based on this measurement, the optimum dead-time is calculated on-the-fly and the DPWM controller will respond immediately to maximize the conversion efficiency in the next switching cycle. This approach is well suited for digital IC implementation. Experimental results from a digitally controlled 6V to 1V, 10A synchronous buck converter verified the efficiency improvement and the practical implementation of the proposed one-step dead-time correction algorithm. This one-step dead-time correction can improve the converter's efficiency by 2 to 4%, depending on output current, output voltage and switching frequency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.006
GPT teacher head0.198
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2010
Admission routes1
Has abstractyes

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