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Record W2085936349 · doi:10.1109/iscas.2014.6865695

Design and validation of a novel reconfigurable and defect tolerant JTAG scan chain

2014· article· en· W2085936349 on OpenAlexafffund
Yves Blaquière, Yan Basile-Bellavance, Safa Berrima, Yvon Savaria

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsPolytechnique MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCMC Microsystems
KeywordsScan chainComputer scienceEmbedded systemRouting (electronic design automation)Computer hardwareBoundary scanIntegrated circuitOperating system

Abstract

fetched live from OpenAlex

In this paper, a novel technique to get a defect tolerant JTAG compliant scan chain in very large area integrated circuits (VLAIC) is presented. It was ruled that wafer-scale VLAICs require structural regularity and defect-tolerance to be cost effective. Using only one scan chain, as typically used in PCBs, would make the whole VLAIC unusable if a single defect is present in the chain. The proposed technique regularly distributes JTAG Test Access Port (TAP) controllers with test data ports linked to two or more neighbor test data ports. One TAP controller is wired as the entry point and another as the exit point of the scan chain that must be configured according to defect locations. An externally controlled wormhole like routing algorithm can be used for functional link discovery. This paper also proposes a mechanism to make defect tolerant access to test data registers, controlled from neighbor TAP controllers. Our technique has been successfully implemented and validated in a wafer-scale like integrated circuit used in a platform for electronic system prototyping. The logic area of this defect-tolerant configurable JTAG scan chain technique occupies 5% of the test logic and 0.3 % of the cell logic when links to four nearest neighbors are included.

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)0.001
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 score0.937
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.227
Teacher spread0.194 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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