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Record W2104359803 · doi:10.1109/vtest.1992.232761

Recent advances in logic synthesis with testability

2003· article· en· W2104359803 on OpenAlexaff
Janusz Rajski, J. Vasudevamurthy, Aiman H. El‐Maleh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsMcGill University
Fundersnot available
KeywordsTestabilityComputer scienceDesign for testingLogic optimizationLogic synthesisSequential logicClass (philosophy)Boolean functionProcess (computing)FactorizationDecompositionLogic gateComputer engineeringTheoretical computer scienceAlgorithmReliability engineeringProgramming languageArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The primary consideration in the entire logic synthesis process is the quality of the resulting circuit measured by its speed, chip area, and recently also testability. The crucial phase in automatic logic synthesis, where all these parameters are determined, is the process of decomposition and factorization which generates multilevel Boolean equations for the synthesized circuit. There are a number of various aspects of testability. These aspects depend on the fault models and testing strategies used. One of the basic objectives is to synthesize circuits that are completely testable for a given class of faults.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 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 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 score0.952
Threshold uncertainty score0.192

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 science0.0000.000
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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations4
Published2003
Admission routes1
Has abstractyes

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