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Record W2127410823 · doi:10.1109/pesc.2008.4592544

A simple switching loss model for buck voltage regulators with current source drive

2008· article· en· W2127410823 on OpenAlexaff
Wilson Eberle, Zhiliang Zhang, Yan‐Fei Liu, Sen Paresh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsInductanceSpiceVoltage sourceControl theory (sociology)VoltageVoltage regulatorWaveformBuck converterCurrent sourceLow-dropout regulatorComputer scienceSimple (philosophy)PiecewiseElectronic engineeringDropout voltageEngineeringElectrical engineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

A review of switching loss mechanisms for synchronous buck voltage regulators is presented. Following the review, a new simple analytical switching loss model is proposed for voltage regulators with current source drive. The model includes the impact of common source inductance and parasitic inductance on switching loss. It uses simple equations to calculate the rise and fall times and piecewise linear approximations of the MOSFET voltage and current waveforms to allow quick and accurate calculation of switching loss. Spice is used to demonstrate the accuracy of the model operating in a 1MHz synchronous buck voltage regulator at 12V input, 1.3V output. Experimental results are presented to demonstrate the accuracy of the proposed model.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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