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Record W2898723422 · doi:10.23919/ipec.2018.8507919

Capacitive Divider Based Passive Start-up Methods for Flying Capacitor Step-down DC-DC Converter Topologies

2018· article· en· W2898723422 on OpenAlexaff
Michael Halamicek, Tom Moiannou, Nenad Vukadinovic, Aleksandar Prodić

Bibliographic record

Venue2018 International Power Electronics Conference (IPEC-Niigata 2018 -ECCE Asia) · 2018
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapacitorElectrical engineeringConvertersVoltageVoltage dividerCapacitive sensingElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper introduces two methods for limiting the voltage stress during start-up across switches of multi-level flying capacitor (ML-FC) step-down dc-dc converters. For a general N-level converter, the presented methods reduce the voltage stress to (N-1) times lower value than that of a conventional buck, allowing lower voltage rating transistors with smaller specific on-resistances to be used. These methods require no active control of switches on initial start-up and rely on segmentation of the input filter capacitor or the utilization of flying capacitors as part of voltage dividers. The speed of these schemes is limited only by the size of the flying capacitor, parasitics in the conduction pathway, and the quality of the start-up diode and the low side switch body diodes. The methods have been verified on a 3-level buck 24V-to-5V, 20W prototype, with input voltage rise times of less than 1µs showing the effectiveness of the start-up circuits.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.318
Teacher spread0.277 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2018
Admission routes1
Has abstractyes

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Same venue2018 International Power Electronics Conference (IPEC-Niigata 2018 -ECCE Asia)Same topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207