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Record W1925665717 · doi:10.1109/iscas.1989.100625

The design of a high-resolution CMOS comparator

2003· article· en· W1925665717 on OpenAlexaff
C.P. Chong, K.C. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComparatorSpiceCMOSCapacitanceCoupling (piping)VoltageElectronic engineeringPhysicsCharge (physics)Capacitive couplingTopology (electrical circuits)Resolution (logic)Computer scienceElectrical engineeringMaterials scienceEngineeringArtificial intelligenceElectrodeParticle physics

Abstract

fetched live from OpenAlex

The authors describe the design of high resolution CMOS comparators by firstly presenting an analysis of comparators with and without the cross-multiplexed technique (XMT). Analytic formulas for the comparison time and the setup time of the comparator are derived. The comparison times calculated are very close to those obtained using SPICE simulation for large coupling capacitances between stages. For small coupling capacitances, the effect of charge-pumping becomes significant and leads to a deviation of the predicted comparison times from those obtained using SPICE simulation. However, the use of XMT, which increases the effective input voltage and thus reduces the charge-pumping effect, leads to a closer agreement between the calculated and simulated comparison times, as well as a significant reduction in the comparison time for cases with small coupling capacitance, where the magnitude of error voltage due to charge pumping is larger. A design example shows that it is possible to implement a CMOS comparator with an input-voltage resolution of less than 100 mu V and a maximum comparison time of less than 2 mu s using XMT.>

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.200
Teacher spread0.179 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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