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Record W2614762055 · doi:10.1109/apec.2017.7930685

A low-volume multi-phase interleaved Dc-Dc converter for high step-down applications with auto-balancing of phase currents

2017· article· en· W2614762055 on OpenAlex
Samuel da Silva Carvalho, S. M. Ahsanuzzaman, Aleksandar Prodić

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInductorRippleElectronic engineeringComputer scienceVoltageBoost converterPower (physics)Electrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper introduces a multi-phase interleaved dc-dc converter, targeted for high-step down applications including voltage regulator modules (VRM) for microprocessors, where low volume and high efficiency are the key priorities. The introduced architecture, reducing the voltage swing at the switching node, along with doubling the effective switching frequency of the inductor current ripple, allows reducing the size of the inductors by up to 4 times, resulting in superior dynamic regulation, while maintaining high power processing efficiency of above 90%. Along with the interleaved 2-phase operation of the introduced converter, practical implementation details, including gate driver implementation, startup, and additional features such as automatic phase current balancing, are also addressed. Experimental verifications with a 12-to-1.2 V, 10 A, 250 kHz prototype show proper functionality of the introduced converter.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.291
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

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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