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Record W2160465664 · doi:10.1109/ats.1997.643989

Built-in self-test for multi-port RAMs

2002· article· en· W2160465664 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsPort (circuit theory)Computer scienceBuilt-in self-testEmbedded systemEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

Most multi-port memory BIST algorithms treat the memory as multiple individual single-port memories and test each independently using the algorithms developed for single-port RAMs. A major problem with this approach is the lack of coverage for multi-port specific defects, such as inter-port interferences due to shorts across ports. This paper proposes a novel BIST algorithm for multi-port RAMs that detects both The conventional single-port faults as well as inter-port shorts. The proposed algorithm performs a conventional single-port test such as MARCH (1991) or SMARCH (1990) on one port of the memory and simultaneously performs an inter-port test on all other ports. The algorithm does not impose any extra test time and requires the addition of only a few gates to a conventional single-port BIST controller, independently of the size of the memory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.307

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 head0.068
GPT teacher head0.274
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

Quick stats

Citations19
Published2002
Admission routes1
Has abstractyes

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