Evaluation of Entropy Driven Compression Bounds on Industrial Designs

Alampally, S.; Abraham, J.A.; Parekhji, Rubin; Kapur, R.; Williams, T.W.

doi:10.1109/ats.2008.89

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…For Design D, as an example, an area overhead of 0.49% can reduce pattern count and test time by 71% at I SFF−thr = 20. In the table, Columns 5,8,11,and 14 show the number of gates inserted due to DT cells. Note that the number of gates inserted is greater than DT cells because each DT cell is made up few gates.…”

Section: Results and Analysismentioning

confidence: 99%

“…Although effective, this method is not feasible for in-field tests due to the need of an external tester. In order to reduce the need for large memory, works have been proposed to compact test cubes [4]- [8]. As an example, [4] was successful at reducing test data volume through the use of embedded deterministic test which can generate deterministic patter ns with var ious input s equences .…”

Section: Introductionmentioning

confidence: 99%

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LBIST pattern reduction by learning ATPG test cube properties

Contreras

Zhao

Ahmed

et al. 2015

Sixteenth International Symposium on Quality Electronic Design

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Logic built-in self-test (LBIST) is commonly used for testing integrated circuits (ICs) in production and in the field. Due to the random nature of LBIST patterns, activation of random-patternresistant faults requires the application of numerous patterns, thus increasing test time in the field. In this work, we introduce a novel method to reduce LBIST pattern count and LBIST test time. The presented technique collects deterministic pattern properties to guide our deterministic test (DT) cell selection and insertion algorithm. The inserted DT cells enhance LBIST patterns quality and reduce pattern count by introducing deterministic-like properties. Experimental results on academic benchmarks and industry designs demonstrate 30% to 70% reduction in pattern count.978-1-4799-7581-5/15/$31.00

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LBIST pattern reduction by learning ATPG test cube properties

Contreras

Zhao

Ahmed

et al. 2015

Sixteenth International Symposium on Quality Electronic Design

View full text Add to dashboard Cite

show abstract

“…In [11], the general entropy theory has been adopted and extended via a best-possible (entropy minimizing) don't care filling technique, in order to iden tify the theoretically achievable compression levels for fixed length symbols. For more contemporary types of decompres sors that are widely utilized in industry, the care-bit density in test cubes is typically utilized to project the attainable com pression ratio [12,13]. In [14], an incremental analysis is provided for a quick generation of test patterns for fanout based decompressors, by re-using the undetected fault set of one configuration to quickly obtain that for another configura tion.…”

Section: Sobeeh Almukhaizimmentioning

confidence: 99%

Predictive analysis for projecting test compression levels

Sinanoglu

Almukhaizim

2010

2010 IEEE International Test Conference

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Test data compression is widely employed in scan designs to tackle high test data volume and test time problems. Given the number of scan-in pins available in the ATE, architectural decisions regarding the number of internal scan chains di rectly impact the compression level attained. While targeting an aggressive compression level by increasing the number of internal scan chains would reduce the test data volume per encodable pattern, the cost of applying more patterns serially, to restore the coverage loss, offsets the compression benefits. Therefore, a predictive analysis is necessary to determine the best possible compression configuration, enabling the design ers to make DjT architectural decisions early on in the design cycle to minimize test costs. In this paper, we propose a suite of predictive techniques geared towards projecting test cost for any given compression-based scan configuration. The ap propriate technique is selected by designers based on which stage the design is in, the design abstraction and the amount of information available, the permissible computational com plexity of the techniques, and the accuracy of the projected optimal compression ratio.

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