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Record W2983111368 · doi:10.1109/19.850413

Test-set embedding based on width compression for mixed-mode BIST

2000· article· en· W2983111368 on OpenAlexaff
Krishnendu Chakrabarty, S.R. Das

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2000
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBuilt-in self-testBenchmark (surveying)Test compressionAutomatic test pattern generationTest setEmbeddingComputer scienceElectronic circuitSet (abstract data type)Generator (circuit theory)Fault coverageAlgorithmEmbedded systemEngineeringPower (physics)Electrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

We present a new test generator circuit (TGC) for mixed-mode built-in self-test (BIST) that embeds a precomputed deterministic test set T/sub D/ in a longer sequence. The design method employs width compression based on the property of d-compatibles. To demonstrate the feasibility of the TGC design methods, we present experimental data for single stuck-at test sets for the ISCAS 85 circuits and full-scan versions of the ISCAS 89 benchmark circuits. We also achieve significant improvement over another recently-proposed mixed-mode TGC design scheme for BIST.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.292
Teacher spread0.247 · 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

Citations14
Published2000
Admission routes1
Has abstractyes

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