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Record W2150555103 · doi:10.1109/iscas.2007.378706

Electromagnetic Compatibility Modeling in Low-Noise Medical Sensor Interfaces

2007· article· en· W2150555103 on OpenAlexafffund
Olivier Valorge, Benoit Gosselin, Louis‐François Tanguay, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsElectromagnetic compatibilityAmplifierSpiceElectronic engineeringCMOSLow-noise amplifierSignal integrityElectrical engineeringComputer scienceChipEngineeringPrinted circuit board

Abstract

fetched live from OpenAlex

Investigations on the electromagnetic behaviour of a low-power amplifier are led using the Extended-Integrated Circuit Emission Model (ICEM). This modeling approach is proposed on a mixed-signal (analog/digital) CMOS 0.18 μm circuit dedicated to neural signal recording. This ICEM allows coarse and fast studies of the electromagnetic compatibility of CMOS devices especially in characterizing the coupling phenomena that occurs at each building block inside the whole chip. ICEM simulations of power and ground bounces are more than 500 times faster than complete SPICE ones with a correct accuracy for first electromagnetic compatibility investigations. This quick modeling method allows for checking many different design or simulation configurations. For example, some simulation results show that substrate interactions and power/ground crosstalk increase the noise level of the low-noise amplifier, in particular in its low frequency domain.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

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

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 head0.230
Teacher spread0.214 · 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 teacher head, 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

Citations2
Published2007
Admission routes2
Has abstractyes

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