240_pdfsam_VLSI TEST PRINCIPLES & ARCHITECTURES

240_pdfsam_VLSI TEST PRINCIPLES & ARCHITECTURES -...

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Test Generation 209 Fitness Evaluation Selection Crossover Mutation 11010000 00101011 01001110 10011101 01111101 1 2 3 4 n Generation 1 1 2 3 4 n 01101110 10111010 10000101 00101111 11010011 Generation 0 ± FIGURE 4.38 GA framework. One simple application of GAs for test generation is to select the best test vectors for each GA run. A simple view of a GA framework is illustrated in Figure 4.38. The test generator starts with a random population of n individuals, and a (fault) simulator is used to calculate the fitness of each individual. The best test vector evolved in any generation is selected and added to the test set. Then, the fault set is updated by removing the detected faults by the added vector(s). The GA process repeats itself until no more faults can be detected. Because a new random population is used initially, the GA process may not guarantee that a successful vector can be found. Likewise, in sequential circuits, a number of vectors may be necessary to drive the circuit to a state before the fault
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This note was uploaded on 05/16/2011 for the course ENGINEERIN mp108 taught by Professor Elbarki during the Spring '08 term at Alexandria University.

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