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Record W1985637037 · doi:10.1109/mwscas.2011.6026336

An ultra-low power voltage level shifter for passive wireless microsystems

2011· article· en· W1985637037 on OpenAlexaff
Xiongliang Lai, Fei Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectrical engineeringCapacitive power supplyVoltageVoltage optimisationPower supply rejection ratioVoltage dividerSwitched-mode power supplyElectronic engineeringAmplifierLow voltageLogic levelEngineeringComputer scienceConstant power circuitCMOS

Abstract

fetched live from OpenAlex

An ultra-low power voltage level shifter that can shift a unipolar voltage to a bipolar voltage is proposed. A stable voltage swing at the output of the voltage level shifter is obtained by injecting a compensation current. To interface with other circuits with a higher supply voltage, a 2-stage dual-supply voltage bridging amplifier is also proposed. The supply voltages of the inverter-based bridging amplifier are carefully chosen such that the static power consumption of the inverters is minimized. Simulation results demonstrated that the power consumption of the proposed voltage level shifter is just a few nWs and the power consumption of the voltage bridging amplifier is within a few μWs, making it particularly suitable for passive wireless microsystems such as RFIDs and biomedical applications.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.221
Teacher spread0.204 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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