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Permanent link (DOI): https://doi.org/10.7939/R3542JM7M

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

Descriptions

Other title
Subject/Keyword
ATPG
GA
PSO
DE
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Alateeq, Majed M
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Examining committee member and department
Musilek, Petr (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Pedrycz, Witold (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Computer Engineering
Date accepted
2016-11-30T13:57:36Z
Graduation date
2017-06:Spring 2017
Degree
Master of Science
Degree level
Master's
Abstract
In automated test pattern generation (ATPG), test patterns are automatically generated and tested against all specific modeled faults, such as stuck-at fault, which is most commonly used in fault modeling. Testing of sequential circuits can be performed exhaustively, randomly or algorithmically. Exhaustive and random test pattern generators consume a high percentage of resources which make them impractical solutions, especially for large sequential circuits. Moreover, the testing time increases rapidly as the number of inputs, or the circuit’s complexity increases, which means that these types of tests are ineffective and cannot be fully adapted. Since the test pattern generation is a search process completed over a large search space, algorithmic test pattern generation is a favorable option because of its ability to reduce the size of the search space, which leads to lowering the number of test patterns and reducing the testing time. The objective of this work is to present a complete analytical study of ATPG for sequential circuits using algorithmic test pattern generators. Three optimization algorithms, namely: genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE), were analytically studied for the purpose of generating optimized test sequence sets. Furthermore, this work 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. Moreover, the recommendations will allow for faster convergence toward optimal solution sets when being implemented for similar applications.
Language
English
DOI
doi:10.7939/R3542JM7M
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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