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A Hybrid 4<sup>th</sup>-Order 4-Bit Continuous-Time ΔΣ Modulator in 65-nm CMOS Technology

2020· article· en· W3047632481 on OpenAlexaff
Ningcheng Gaoding, Jean‐François Bousquet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIntegratorOversamplingOperational amplifierDelta-sigma modulationCMOSElectronic engineeringDigital biquad filterBandwidth (computing)Effective number of bitsFigure of meritConvertersComputer scienceFlash ADCElectrical engineeringAmplifierEngineeringLow-pass filterTelecommunicationsComparatorVoltage

Abstract

fetched live from OpenAlex

This paper reports a fourth-order continuous-time (CT) delta-sigma modulator (DSM) that features a single biquad integrator, a passive integrator and an active integrator. A benefit is the low power consumption using only two opamps in comparison to 4 power-hungry opamps in the conventional fourth-order DSM. The proposed CT-DSM employs two Miller compensation opamps to satisfy the gain bandwidth (GBW) requirement and the loop gain requirement. In this design, the GBW is only 1.65 times higher than the sampling frequency and the open loop DC gain is much higher than the oversampling rate. A 4-bit flash analog-to-digital converter (ADC) and two feedback digital-to-analog converters (DACs) are employed in this design to complete the CT DSM including the feedback paths. The effective number of bits of the proposed CT-DSM is 14 bits with a peak SNR of 90.5 dB. The proposed design has a maximum bandwidth of 2 MHz with a power consumption less than 3 mW. Thus, it achieves an excellent figure of merit around 175 dB compared to existing state-of-the-art.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.174
Teacher spread0.167 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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