Analysis of Cellular Automata and Genetic Algorithm based Test Pattern Generators for Built In Self Test
In today’s semiconductor industry, the increasing growth of sub-micron technology has resulted in the difficulty of VLSI testing. The biology is a rich source of inspiration for designers to solve the problems related to VLSI testing such as high fault coverage, less test time, efficient test pattern generation and to reduce the power consumption during testing. The main goal of this paper is to analyze the bio-inspired test pattern generation mechanisms such as Genetic algorithms and cellular automata for the built in self test. Here we have introduced the concept of cellular automata, and analyzed the parameters (like area and power) obtained from the simulation results of cellular automata and LFSR (type I, II). The experiments are performed for the Genetic algorithm, Random and deterministic cellular automata Test Pattern generation for combinational ISCAS 85 and sequential ISCAS 89 benchmark circuits. Experimental results show that more fault coverage is achieved with less Test Vectors with adequate time.
KeywordsBuilt in self test Cellular automata LFSR Genetic algorithms
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