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Record W2798605984 · doi:10.1109/apec.2018.8340990

A multi-level, multi-phase buck converter with shared flying capacitor for VRM applications

2018· article· en· W2798605984 on OpenAlexaff
Gianluca Roberts, Nenad Vukadinovic, Aleksandar Prodić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapacitorInductorTopology (electrical circuits)Buck converterVoltageElectrical engineeringSwitched capacitorBuck–boost converterComputer scienceElectronic engineeringLow-dropout regulatorVoltage regulatorEngineeringDropout voltage

Abstract

fetched live from OpenAlex

This paper introduces a multiphase high step-down DC-DC converter that is suitable for voltage regulator module (VRM) applications. In particular, this converter is derived from the conventional flying capacitor multilevel converter. Despite being a multiphase topology, the converter contains just a single flying capacitor that reduces voltage swing at the inductor switching nodes. Furthermore, all switches experience a maximum voltage stress of half the input voltage providing benefits such as the use of transistors with better figures of merit (FOM), as well as reduced switching losses. The converter is well suited for high conversion ratios and high load output required by VRMs as the freewheeling path of each phase contains only one switch. The effectiveness of this topology is verified with an experimental prototype with wide input voltage 12V/24V, and load Vout= 1.2 V, Iout≤ 40 A operating at 500 kHz switching frequency. Peak efficiencies of 93.2% at 16A, and 89.4% at 28A, are attained with inputs at 12 V and 24V, respectively.

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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.299
Teacher spread0.245 · 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
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

Citations27
Published2018
Admission routes1
Has abstractyes

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