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Record W2038999107 · doi:10.1109/ccece.2008.4564489

An efficient first order sigma delta modulator design

2008· article· en· W2038999107 on OpenAlexvenueno aff
Nowshad Amin, Goh Chit Guan, Ibrahim Ahmad

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
Fundersnot available
KeywordsDelta-sigma modulationOversamplingSlew rateIntegratorJitterElectronic engineeringPhase marginBandwidth (computing)CMOSOperational amplifierNoise shapingAmplifierElectrical engineeringPhysicsComputer scienceEngineeringVoltageTelecommunications

Abstract

fetched live from OpenAlex

An efficient first order sigma delta modulator has been designed in circuit level, considering the possible non-idealities in 65 nm CMOS technology. This study at first determines the non-idealities of sigma delta modulator. The non-idealities investigated here are clock jitter noise that effects the input signal and increases total error power; then the thermal noise of switches caused by the random fluctuation of carrier that increases the total noise power. Thereafter, circuit leakage causes the limited DC gain and affects signal to noise ratio. Moreover, limited slew rate and gain bandwidth of op-amp, which are both regarded as non-linear gain, reduce signal to noise sum distortion ratio. Based on optimum circuit simulation, the non-idealities are reduced by using folded cascode op-amp at integrator stage with DC gain of 65 dB, slew rate of 3.76 V/μs, and gain bandwidth with 40 MHz. Finally, a first order sigma delta modulator with 8 bit resolution, 64 oversampling ratio as well as power supply of ±2.5 V is successfully designed using PSPICE simulation tool, which can be implemented for practical usage.

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: Bench or experimental · Consensus signal: Bench or experimental
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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.020
GPT teacher head0.179
Teacher spread0.159 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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