Partitioning sequential circuits for pseudoexhaustive testing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on the 11th international symposium on system-level synthesis and design (ISSS'98)
Partitioning algorithm to enhance pseudoexhaustive testing of digital VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on system-level interconnect prediction
Swarm intelligence
An Efficient Partitioning Algorithm of Combinational CMOS Circuits
ISVLSI '02 Proceedings of the IEEE Computer Society Annual Symposium on VLSI
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Investigation of particle swarm optimization for switching characterization of inverter design
Expert Systems with Applications: An International Journal
PID-type fuzzy logic controller tuning based on particle swarm optimization
Engineering Applications of Artificial Intelligence
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This paper presents a swarm intelligence based approach to optimally partition combinational CMOS circuits for pseudoexhaustive testing. The partitioning algorithm ensures reduction in the number of test vectors required to detect faults in VLSI circuits. The algorithm is based on the circuit's maximum primary input cone size (N) and minimum fanout (F) values to decide the location and number of partitions. Particle swarm optimization (PSO) is used to determine the optimal values of N and F to minimize the number of test vectors, the number of partitions, and the increase in critical path delay due to the added partitions. The proposed algorithm has been applied to the ISCAS'85 benchmark circuits and the results are compared to other partitioning approaches, showing that the PSO partitioning algorithm produces similar results, approximately one-order of magnitude faster.