Analysis of Optimization Algorithms in Automated Test Pattern Generation for Sequential Circuits

Alateeq, Majed and Pedrycz, Witold (2017) Analysis of Optimization Algorithms in Automated Test Pattern Generation for Sequential Circuits. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). pp. 1834-1839.

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Official URL: http://ieeexplore.ieee.org/document/8122883/

Abstract|ملخص البحث

In automated test pattern generation (ATPG), test patterns are automatically generated and tested against all specific modeled faults. In this work, three optimization algorithms, namely: genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE), were studied for the purpose of generating optimized test sequence sets. Furthermore, this paper investigated the broad use of evolutionary algorithms and swarm intelligence in automated test pattern generation to expand the analysis of the subject. The obtained experimental results demonstrated the improvement in terms of testing time, number of test vectors, and fault coverage compared with previous optimization-based test generators. In addition, the experiments highlight the weakness of each optimization algorithm in the test pattern generation (TPG) and offer some constructive methods of improvement. We present several recommendations and guidelines regarding the use of optimization algorithms as test pattern generators to improve the performance and increase their efficiency.

Item Type|تصنيف النتاج العلمي: Article| منشور علمي
Subjects | مجال موضوع النشر: Computer
Computer Engineering
Divisions | الكلية: College of Computer Sciences > Computer Engineering
Depositing User: Majed Alateeq
Date Deposited: 16 Feb 2018 16:12
Last Modified: 16 Feb 2018 16:12
URI: http://eprints.kku.edu.sa/id/eprint/3363

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