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Record W2117707690 · doi:10.1109/tpel.2011.2175453

Adaptive Current Source Drivers for Efficiency Optimization of High-Frequency Synchronous Buck Converters

2011· article· en· W2117707690 on OpenAlex
Zhiliang Zhang, Jizhen Fu, Yan‐Fei Liu, Paresh C. Sen

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

VenueIEEE Transactions on Power Electronics · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsConvertersElectronic circuitVoltageControl theory (sociology)Current sourceBuck converterVoltage sourceComputer scienceCurrent (fluid)Electronic engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, the concept of the adaptive current source drivers (CSDs) is proposed for the high-frequency synchronous buck converters. Compared to the previous CSD circuits, the adaptive CSD can achieve adjustable drive current and drive voltage according to different load condition. The benefit is that higher drive current and voltage lead to lower switching loss and conduction loss when the load current increases. Therefore, the adaptive CSD is able to realize optimal design to reduce the switching loss, the gate drive loss, and the conduction loss in a wide load range. It should be noted that the adaptive concept is suitable for both the continuous and discontinuous CSDs regardless the drive circuit topologies. Through investigating the CSD circuits, one simple method to achieve the adaptive drive current based on the adaptive voltage is proposed. A 12 V input, 1.3 V output, and 1-MHz synchronous buck converter with the continuous and discontinuous CSDs was built, respectively, to verify the advantages of the proposed adaptive concept and efficiency improvement.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.984
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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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