On static compaction of test sequences for synchronous sequential circuits
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Comparing Topological, Symbolic and GA-based ATPGs: An Experimental Approach
Proceedings of the IEEE International Test Conference on Test and Design Validity
Dynamic test compaction for synchronous sequential circuits using static compaction techniques
FTCS '96 Proceedings of the The Twenty-Sixth Annual International Symposium on Fault-Tolerant Computing (FTCS '96)
HITEC: a test generation package for sequential circuits
EURO-DAC '91 Proceedings of the conference on European design automation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient Techniques for Dynamic Test Sequence Compaction
IEEE Transactions on Computers
Journal of Electronic Testing: Theory and Applications
Proceedings of the conference on Design, automation and test in Europe
8.2 On Synchronizing Sequences and Test Sequence Partitioning
VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
Efficient Static Compaction of Test Sequence Sets through the Application of Set Covering Techniques
Proceedings of the conference on Design, automation and test in Europe - Volume 1
A New Approach to Test Generation and Test Compaction for Scan Circuits
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
SPIN-PAC: test compaction for speed-independent circuits
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Search space pruning techniques in ATPG for VLSI circuits
ICC'05 Proceedings of the 9th International Conference on Circuits
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This paper describes an algorithm for compacting the Test Sequences generated by an ATPG tool without reducing the number of faults they detect. The algorithm is based on re-ordering the sequences so that some of them can be shortened and some others eliminated. The problem is NP-complete, and we adopt Genetic Algorithms to obtain optimal solutions with acceptable computational requirements. As it requires just one preliminary Fault Simulation experiment, the approach is much more efficient than others proposed before; experimental results gathered with Test Sets generated by different ATPG tools show that the method is able to reduce the size of the Test Set by a factor varying between 50% and 62%.