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Record W1522804467 · doi:10.1109/newcas.2015.7182025

A power-efficient wide-range signal level-shifter

2015· article· en· W1522804467 on OpenAlexafffund
Esmaeel Maghsoudloo, M. Rezaei, Mohamad Sawan, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique MontréalUniversité Laval
FundersUniversité Laval
KeywordsLogic levelCMOSElectrical engineeringSIGNAL (programming language)Subthreshold conductionCapacitorVoltageElectronic engineeringTopology (electrical circuits)Low-power electronicsPower (physics)Computer scienceEngineeringPhysicsTransistorPower consumption

Abstract

fetched live from OpenAlex

In this paper, we propose a level shifter circuit that is able to convert signal levels of subthreshold values to super-threshold signal levels. Such a circuit is using a new voltage level shifter topology employing a level-shifting capacitor. This capacitor is charged only when the logic levels of the input and output signals are not corresponding to a high-to-low transition of the input signal. The proposed circuit has a small silicon area, low-power consumption and short propagation delay. Post-layout Simulation results for the proposed circuit implemented in a 0.18-μm 1P6M CMOS technology confirm that the power consumption of this circuit is extremely low comparing to other topologies, and is able to operate over a wide range of the input voltages from 50 mV to 1.8 V, and a wide range of frequencies from 100 Hz to 100 MHz. For both a 0.4 V and a 1.8V supply voltages, the proposed circuit has a propagation delay of 10.43 ns and a power consumption of 9.89 nW for a 10-kHz input signal.

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 score0.003
Threshold uncertainty score0.010

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)0.0030.001

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 head0.041
GPT teacher head0.216
Teacher spread0.175 · 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
Published2015
Admission routes2
Has abstractyes

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