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Record W1624047868 · doi:10.1109/iscas.1993.394044

Towards built-in-self-test for SNR testing of a mixed-signal IC

2002· article· en· W1624047868 on OpenAlexafffund
M.F. Toner, Gordon W. Roberts

Bibliographic record

Venue1993 IEEE International Symposium on Circuits and Systems · 2002
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixed-signal integrated circuitComputer scienceBuilt-in self-testVery-large-scale integrationAnalog signalChipNoise (video)MicroprocessorSIGNAL (programming language)Digital signalElectronic engineeringAnalogue electronicsAnalog-to-digital converterSignal-to-noise ratio (imaging)Computer hardwareIntegrated circuitEmbedded systemDigital signal processingEngineeringElectronic circuitElectrical engineeringArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Built-in-self-test (BIST) for digital systems has become feasible for a variety of digital VLSI systems. However, mixed analog-digital BIST (MADBIST) has not yet achieved the same degree of practicality. An accurate analog signal source, as well as circuitry to measure the analog response, must be fabricated onto the chip, and must be self-calibrating. A MADBIST is discussed which could be used for a signal-to-noise ratio test on a chip which includes a digital-to-analog converter, an analog-to-digital converter, and a microprocessor which includes RAM and ROM. The MADBIST strategy for the SNR test is introduced, accuracy issues are discussed, and simulation results are presented.>

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.237
Teacher spread0.203 · 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
GenreMethods

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

Citations8
Published2002
Admission routes2
Has abstractyes

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Same venue1993 IEEE International Symposium on Circuits and SystemsSame topicIntegrated Circuits and Semiconductor Failure AnalysisFrench-language works237,207