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Record W2155411941 · doi:10.1109/jssc.2006.873889

A Time-Interleaved Continuous-Time<tex>$DeltaSigma$</tex>Modulator With 20-MHz Signal Bandwidth

2006· article· en· W2155411941 on OpenAlexafffund
Trevor Caldwell, D.A. Johns

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2006
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsOversamplingDelta-sigma modulationIntegratorDynamic rangeBandwidth (computing)Electronic engineeringCMOSOperational amplifierEngineeringAmplifierTelecommunications

Abstract

fetched live from OpenAlex

This paper presents the first implementation results for a time-interleaved continuous-time DeltaSigma modulator. The derivation of the time-interleaved continuous-time DeltaSigma modulator from a discrete-time DeltaSigma modulator is presented. With various simplifications, the resulting modulator has only a single path of integrators, making it robust to DC offsets. A time-interleaved by 2 continuous-time third-order low-pass DeltaSigma modulator is designed in a 0.18-mum CMOS technology with an oversampling ratio of 5 at sampling frequencies of 100 and 200 MHz. Experimental results show that a signal-to-noise-plus-distortion ratio (SNDR) of 57 dB and a dynamic range of 60 dB are obtained with an input bandwidth of 10 MHz, and an SNDR of 49 dB with a dynamic range of 55 dB is attained with an input bandwidth of 20 MHz. The power consumption is 101 and 103 mW, respectively

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 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

Citations41
Published2006
Admission routes2
Has abstractyes

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