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A Rail-to-Rail Low-Power Dynamic CMOS Amplifier for Switched-Capacitor Filters in High-Performance ADC

2024· article· en· W4402727340 on OpenAlexaff
Antoine Verreault, Paul-Vahé Cicek, Alexandre Robichaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsSwitched capacitorCMOSAmplifierCapacitorElectrical engineeringElectronic engineeringComputer scienceEngineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents a novel rail-to-rail low power dynamic CMOS amplifier optimized for discrete-time filtering in analog-to-digital converters (ADC). The proposed architecture incorporates a switched resistor-capacitor (RC) parallel compensation technique, which is strategically activated in the sampling phase to minimize the amplifier's current consumption. Dynamic biasing of the amplifier further improves energy efficiency by slashing high slewing currents once linear settling takes over. Simulation results in 65 nm CMOS exhibit 3.2 times reduction in power consumption with respect to a comparable static amplifier. Accurate integration waveforms are achieved while consuming a mere 239 μW for 6 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> settling under 4.9 ns.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.985

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)0.0000.001

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 head0.007
GPT teacher head0.212
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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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