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Record W2077301215 · doi:10.1109/mwscas.2006.381981

An Ultra-Low-Power Successive-Approximation-Based ADC for Implantable Sensing Devices

2006· article· en· W2077301215 on OpenAlex
Pierre-Yves Robert, Benoit Gosselin, Amer Elias Ayoub, Mohamad Sawan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2006 49th IEEE International Midwest Symposium on Circuits and Systems · 2006
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSuccessive approximation ADCCapacitorEffective number of bitsCMOSConvertersElectronic engineeringAnalog-to-digital converterShapingPower (physics)VoltageElectrical engineeringDissipationComputer scienceLow voltageLow-power electronicsSampling (signal processing)Switched capacitorEngineeringPhysicsPower consumption

Abstract

fetched live from OpenAlex

Small area and power efficient analog-to-digital converters are needed to accommodate implantable multichannel sensors. This paper concerns the design and implementation of an ultra-low-power 8-bit successive approximation analog-to-digital converter (ADC). A new topology is proposed which improves the conventional architectures by replacing the successive approximation register and the digital-to-analog converter by a switchedopamp switched-capacitor circuit which dissipates very low power. The sampling frequency of the proposed ADC is 30 kSps and within an input range of 600 mV. Implemented in a 0.18-μm CMOS process with a 1.8 V voltage supply, the ADC achieves a power dissipation of 7.4 μW and occupies an area of 0.04 mm2.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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