MétaCan
Menu
Back to cohort
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 OpenAlex

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.
fundA Canadian funder is recorded on the work.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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