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Record W2171251991 · doi:10.1109/apccas.2008.4746105

A very-high output impedance current mirror for very-low voltage biomedical analog circuits

2008· article· en· W2171251991 on OpenAlexafffund
Louis‐François Tanguay, Mohamad Sawan, Yvon Savaria

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Research ChairsCMC Microsystems
KeywordsCurrent mirrorNMOS logicCMOSOutput impedanceElectrical impedanceElectrical engineeringCurrent (fluid)High impedanceVoltageTransistorOverdrive voltagePhysicsComputer scienceElectronic engineeringEngineeringThreshold voltage

Abstract

fetched live from OpenAlex

In this paper, we present the design of a new very-high output impedance CMOS current mirror with enhanced output voltage compliance. The proposed current mirror uses MOS current dividers to sample the output current and a feedback action is used to force it to be equal to the input current; yielding very high impedance with a very large output voltage range. The proposed implementation yields an increase of the output impedance by a factor of about gmrocompared with that of the super-Wilson current mirror, thus offering a potential solution to mitigate the effect of the low output impedance of ultra-deep submicron CMOS transistors used in sub 1-V current mirrors and current sources. A NMOS version of the proposed current mirror circuit was implemented using STMicroelectronics 1-V 90-nm CMOS process and simulated using Spectre to validate its performance. The output current is mirrored with a transfer error lower than 1% down to an output voltage as low as 80 mV for an input current of 5 muA, and 111 mV when the input current is increased to 50 muA.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0020.001
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 head0.028
GPT teacher head0.239
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations29
Published2008
Admission routes2
Has abstractyes

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