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Record W2164533451 · doi:10.1109/test.2001.966723

Shadow write and read for at-speed BIST of TDM SRAMs

2002· article· en· W2164533451 on OpenAlexaff
Yuejian Wu, Liviu Calin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsStatic random-access memoryComputer scienceEmbedded systemComputer hardwareEngineering

Abstract

fetched live from OpenAlex

Time Domain Multiplex (TDM) SRAMs are a new type of multi-port SRAMs. Due to their small area and flexibility, they have found a wide range of applications in telecommunication ASICs. For a TDM SRAM, the memory core runs many times faster than the circuits that access it. In other words, the memory runs at an internal clock that is much faster than the system clock. This slow system clock coupled with the fast internal clock creates new challenges for at-speed testing of TDM SRAMs. This paper proposes a novel BIST solution for at-speed testing of TDM SRAMs with a slow system clock. The solution can be implemented with most commercial BIST controllers for conventional SRAMs. All the required modifications can be included in a modified memory collar The hardware addition is small. The test time is the same as that for a conventional multi-port SRAM of the same size.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.218

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 head0.049
GPT teacher head0.230
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations1
Published2002
Admission routes1
Has abstractyes

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