On optimizing BIST-architecture by using OBDD-based approaches and genetic algorithms

We introduce a two-staged Genetic Algorithm for optimizing weighted random pattern testing in a Built-in-Self-Test (BIST) environment. The first stage includes the OBDD-based optimization of input probabilities with regard to the expected test length. The optimization itself is constrained to discrete weight values which can directly be integrated in a BIST environment. During the second stage, the hardware-design of the actual BIST-structure is optimized. Experimental results are given to demonstrate the quality of our approach.

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