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Record W4233694000 · doi:10.1142/s0218126600000081

NOVEL TEST GENERATION ALGORITHM FOR COMBINATION CIRCUITS

2000· article· en· W4233694000 on OpenAlex

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

VenueJournal of Circuits Systems and Computers · 2000
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of WindsorToronto Metropolitan University
Fundersnot available
KeywordsAutomatic test pattern generationCombinational logicTestabilityAlgorithmTest setFault coverageSet (abstract data type)Fault (geology)Stuck-at faultUpper and lower boundsElectronic circuitEnumerationSequential logicComputer scienceMathematicsLogic gateFault detection and isolationEngineeringDiscrete mathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

It has been known for many years that combinational circuits have a Complete Test Set (CTS) which is capable of detecting all single and multiple faults. In this paper, we attempt to find CTS systematically. Our algorithm finds a test set which detects all single and multiple stuck-at faults in combinational circuits. This test set is obtained without probing internal nodes, using fault simulation or fault enumeration. It is shown that the test set is independent of logic circuit structure and dependent to the mapping function, number of inputs, outputs, and fanout stems. An upper-bound and lower-bound figures for the number of test vectors required to obtain 100% fault coverage are provided. This number is a small fraction of the entire solution space. A number of recommendations are made to improve the testability of a logic circuit.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.029
GPT teacher head0.235
Teacher spread0.206 · 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