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Record W1775380792 · doi:10.1109/iwsoc.2004.23

An intellectual property module for auto-calibration of time-interleaved pipelined analog-to-digital converters

2004· article· en· W1775380792 on OpenAlex
David Morin, F. Normandin, Marie-Eve Grandmaison, Hung Dang, Yvon Savaria, Mohamad Sawan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE International Workshop on System-on-Chip for Real-Time Applications · 2004
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsConvertersOffset (computer science)Computer scienceCompensation (psychology)CalibrationElectronic engineeringProperty (philosophy)EngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

A flexible digital intellectual property (IP) module that controls the auto-calibration of time interleaved pipelined ADCs is presented. It takes advantage of a judicious combination of classical calibration techniques to determine, in an adaptive way, the adequate compensation of gain and offset for each stage of interleaved pipelined ADCs. A novel built-in self-test (BIST) is also included in the IP. Preliminary simulation results confirm the expected behavior of the calibration method. This soft IP was designed and synthesized.

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.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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