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Record W2036409129 · doi:10.1109/ispsd.2014.6856011

An integrated tri-mode non-inverting buck-boost DC-DC converter with segmented power devices and power transmission gate structure

2014· article· en· W2036409129 on OpenAlexaff
Ge Jin, Wai Tung Ng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBuck converterPower (physics)Electrical engineeringOverhead (engineering)Transmission gateComputer scienceTransmission (telecommunications)Gate driverConvertersLogic gatePower transmissionMode (computer interface)VoltageElectronic engineeringTopology (electrical circuits)EngineeringPhysicsTransistor

Abstract

fetched live from OpenAlex

This paper presents a tri-mode non-inverting buck-boost DC-DC converter with segmented power devices, power transmission gate structure and dead-time control to improve the power conversion efficiency and the overall size. The proposed DC-DC converter accepts an input voltage, Vinfrom 2.7V to 5V and produces an output, Voutranging from 1V to 6V. A peak power conversion efficiency of 94% is observed from the simulation result. It is designed to operate in buck, boost or buck-boost mode while maintaining peak efficiency. Unlike existing switch-mode power supplies, a power transmission gate is used as the high-side driver to eliminate external components such as the bootstrap circuit. This would save a significant amount of PCB space at the expense of only 18% silicon area overhead. The proposed IC is designed using Dongbu HiTek's 0.18μm UVCMOS technology.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.204
Teacher spread0.201 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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