Evolutionary Approach to Test Generation for Functional BIST

TL;DR

This paper presents an evolutionary approach to generate functional BIST tests, reducing test data volume by using genetic algorithms and programming techniques to optimize test sequences for microprogrammed devices.

Contribution

It introduces two novel methods for functional test set derivation using genetic algorithms and programming, improving test efficiency and data reduction.

Findings

Genetic algorithm-based method effectively reduces test data volume.

Genetic programming approach generates optimized microoperation sequences.

Experimental results demonstrate the efficiency of proposed methods.

Abstract

In the paper, an evolutionary approach to test generation for functional BIST is considered. The aim of the proposed scheme is to minimize the test data volume by allowing the device's microprogram to test its logic, providing an observation structure to the system, and generating appropriate test data for the given architecture. Two methods of deriving a deterministic test set at functional level are suggested. The first method is based on the classical genetic algorithm with binary and arithmetic crossover and mutation operators. The second one uses genetic programming, where test is represented as a sequence of microoperations. In the latter case, we apply two-point crossover based on exchanging test subsequences and mutation implemented as random replacement of microoperations or operands. Experimental data of the program realization showing the efficiency of the proposed methods are presented.