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Record W2912838827 · doi:10.1109/tcsi.2019.2893861

Dual-Path and Dual-Chopper Amplifier Signal Conditioning Circuit With Improved SNR and Ultra-Low Power Consumption for MEMS

2019· article· en· W2912838827 on OpenAlexafffund
Parisa Vejdani, Frédéric Nabki

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDual (grammatical number)Power consumptionChopperElectrical engineeringPower (physics)AmplifierSignal conditioningSIGNAL (programming language)Path (computing)Electronic engineeringComputer scienceEngineeringPhysicsVoltageCMOS

Abstract

fetched live from OpenAlex

A dual chopper amplifier (DCA) signal conditioning circuit with ultra-low power consumption is presented for microelectromechanical systems transducers. In the first stage, a low voltage high current amplifier is implemented, which improves the power consumption and noise floor. The second stage is composed of two parallel paths that improve SNR and provide two gain settings. To mitigate flicker noise, the amplifiers are chopped at two different frequencies, also providing an additional degree of freedom to the design. The circuit is designed in a 0.13 μm CMOS technology with 0.7 and 1.2 V supplies. The power consumption is of 2.66 μW at the 0.7 V supply and 3.26 μW at the 1.2 V supply. For a 1.6 mV input, in single path mode, the DCA has a gain of 34 dB, a bandwidth of 4 kHz and achieves an SNR of 89.06 dB in the frequency range of 0.5-4 kHz. In dual path mode, the DCA has a gain of 38 dB, a bandwidth of 3 kHz and achieves an SNR of 92.85 dB in the frequency range of 0.5-4 kHz. The effect of the chopper at the second amplifier in the single path and dual path modes is detailed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
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 head0.010
GPT teacher head0.201
Teacher spread0.190 · 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.

Study designBench or experimental
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

Citations8
Published2019
Admission routes2
Has abstractyes

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