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.