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Record W2946291692 · doi:10.1109/tcsi.2019.2914638

A Third-Order Integrated Passive Switched-Capacitor Filter Obtained With a Continuous-Time Design Approach

2019· article· en· W2946291692 on OpenAlexafffund
Sevil Zeynep Lüleç, D.A. Johns, Antonio Liscidini

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2019
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSwitched capacitorCapacitorDecoupling capacitorIntegratorElectronic engineeringElectronic filter topologyFilter (signal processing)Active filterCMOSFilter capacitorTopology (electrical circuits)Electrical engineeringFilter designComputer sciencePrototype filterEngineeringVoltage

Abstract

fetched live from OpenAlex

A third-order passive switched-capacitor low-pass filter is presented together with experimental results. The current input-voltage output filter structure realizes complex-conjugate poles although it is composed of switches and capacitors. The results are verified with measurements performed on the filter prototype integrated in a 0.13-μm CMOS technology. The prototype has a cut-off frequency of 470 kHz, 150-μW power consumption from 1.2-V power supply, 92-dB SFDR, and an active area of 0.06 mm2. The switch-capacitor filter was obtained using a continuous-time model that is also described here and is useful for design, analysis, and simulation of oversampled switched-capacitor circuits. The model is applicable to a variety of topologies including multi-phase passive switched-capacitor filters, switched-capacitor integrators, as well as switched-capacitor dc/dc converters.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.172
Teacher spread0.162 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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