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Record W2989502635 · doi:10.1109/lssc.2019.2951690

A 0.58-to-0.9-V Input 0.53-V Output 2.4-$\mu$ W Current-Feedback Low-Dropout Regulator With 99.8% Current Efficiency

2019· article· en· W2989502635 on OpenAlexafffund
Ziyu Wang, Shahriar Mirabbasi

Bibliographic record

VenueIEEE Solid-State Circuits Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLow-dropout regulatorDropout voltageCapacitorCMOSVoltageVoltage regulatorRegulatorControl theory (sociology)Load regulationDropout (neural networks)Voltage referenceElectrical engineeringPhysicsTopology (electrical circuits)Computer scienceEngineeringChemistryControl (management)

Abstract

fetched live from OpenAlex

This letter presents an output capacitor-less low-dropout regulator (LDO) topology that can operate from 0.58-to-0.9-V supply, and has a minimum dropout voltage of 50 mV. Compared with traditional analog LDOs, the proposed design incorporates a current reference into the regulator loop and uses current feedback to alleviate the design constraints caused by the limited voltage headroom. The LDO is implemented in a 0.13-μm standard-threshold-voltage CMOS process. Measurement results show that depending on the supply voltage the quiescent power consumption is from 2.4 to 3.6 μW, and the current efficiency is 99.8%. The mean value of the output voltage of 16 samples is 0.53 V and the standard deviation is around 4 mV. The load current range of the proposed LDO is from 0 to 3 mA, and it is capable of driving a load capacitor of up to 120 pF.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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Same venueIEEE Solid-State Circuits LettersSame topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207