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Record W2510175554 · doi:10.1109/iscas.2016.7539031

An amplifier-shared inverter-based MASH structure ΔΣ modulator for smart sensor interfaces

2016· article· en· W2510175554 on OpenAlexafffund
Bahareh Honarparvar, Mona Safi-Harb, 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 CanadaCMC Microsystems
KeywordsDelta-sigma modulationIntegratorOperational amplifierElectronic engineeringFeed forwardInverterAmplifierChopperTotal harmonic distortionOperational transconductance amplifierCMOSBandwidth (computing)Computer scienceElectrical engineeringEngineeringVoltageTelecommunications

Abstract

fetched live from OpenAlex

A 0.9 V feed forward, op-amp shared MASH structure delta sigma modulator for low power sensor interface applications in 0.18 μm CMOS process is presented in this paper. A modified feed forward op-amp shared structure is implemented to reduce the integrators output swings. Due to opamp sharing, the power hungry adder, which constitutes a first challenge in conventional feed forward structures when used in lower-power applications, is eliminated. Moreover, the signal transfer function is modified to be equal to unity in each stage, addressing the second challenge in the conventional feed forward architecture. To reduce the power consumption of the modulator, a fully-differential inverter-based operational transconductance amplifier (OTA) is adopted. Flicker noise is alleviated with the adoption of a chopper stabilization technique. The proposed modulator is sampled at 5.12 MHz for a bandwidth of 20 kHz (OSR of 128). Post-layout simulations show that the modulator achieves 91 dB/89 dB SNR/SNDR respectively while consuming only 65 μW of power.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.805

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)0.0010.000

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 head0.220
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 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
Published2016
Admission routes2
Has abstractyes

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