MétaCan
Menu
Back to cohort

A Dual Loop Current Mode Feedback Capacitor Less LDO for High Current Applications

2024· article· en· W4402572095 on OpenAlexaff
Pierre Leduc, Ximing Fu, Yushi Zhou, Kamal El‐Sankary

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsDalhousie UniversityLakehead University
Fundersnot available
KeywordsCapacitorCurrent (fluid)Dual (grammatical number)Dual modeFeedback loopControl theory (sociology)Current mirrorMaterials scienceOptoelectronicsPhysicsVoltageComputer scienceElectrical engineeringElectronic engineeringEngineeringTransistor

Abstract

fetched live from OpenAlex

This paper presents a high speed, high current, capacitor less analog low dropout (LDO) voltage regulator using a dual loop architecture. The voltage feedback coupled with current feedback loop regulating the output is utilized in the proposed design. The voltage feedback provides accurate voltage regulation while the current feedback provides the feature of fast response to wide load changes. A feedforward line filter is employed to improve the power supply rejection ratio (PSRR) by making use of an extra path that directly affects the voltage at the gate of the power transistor. The proposed LDO is designed in a TSMC 180-nm 1.8 V standard CMOS technology. The overall design occupies an active area of 0.0131 mm2and can supply up to 500 mA at 1.2 V output while supporting load capacitance from 0 to 2 nF. During a 500 mA step-load, the transient response time is 3.1 µs with overshoot and undershoot of 132 mV and 156 mV, respectively. The measured PSRR is -43.78 dB at 10 kHz and -23.70 dB at 1 MHz when the line filter is activated.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207