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Record W2170254026 · doi:10.1109/isqed.2007.122

Power, Delay and Yield Analysis of BIST/BISR PLAs Using Column Redundancy

2007· article· en· W2170254026 on OpenAlex
Uthman Alsaiari, Resve Saleh

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 institutionsUniversity of British Columbia
Fundersnot available
KeywordsRedundancy (engineering)Logic blockSpare partBuilt-in self-testApplication-specific integrated circuitEngineeringLogic synthesisEmbedded systemField-programmable gate arrayLogic gateComputer scienceReliability engineeringElectronic engineering

Abstract

fetched live from OpenAlex

As the number of transistors on a chip begins to exceed 1 billion, it is mandatory to use a portion of the transistors for the purposes of built-in-self-test (BIST) and built-in-self-repair (BISR) as part of the supporting circuitry. However, this requires the use of structured logic, such as programmable logic arrays (PLAs) or structured ASIC. In this paper, we select the fastest and lowest energy PLA design to date and combine it with a block duplication strategy to construct a BIST/BISR PLA in order to establish a reference design. Then, we introduce a PLA redundancy in the form of spare columns and carry out a yield analysis. The results of the yield analysis suggest that using duplication for BIST/BISR is better suited for small PLAs while using spares is more suitable for larger PLAs. The spares needed are determined by several factors including target yield, area, power and delay

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.031
GPT teacher head0.272
Teacher spread0.241 · 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

Citations7
Published2007
Admission routes1
Has abstractyes

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