The Combinational Test Generation Algorithm Based on Three- valued Neural Networks

With the growth in size and complexity of integrated circuits, test generation for them is becoming increasingly difficult, so it is important to find new and effective digital integrated circuit test generation algorithm. In order to improve the quality of combinational test generation, a combinational circuits test generation algorithm based three-valued neural networks [1] is proposed in this paper. This algorithm does not need propagation and backtracks, but represents the combinational circuits as a bidirectional network of neurons using the three-valued neural networks, and constructs the energy function for the network. The application of three-valued neural works may reduce research space and avoid many wasteful assignments, improve the efficiency of combinational test generation. A genetic algorithm was used to find the global minimal as the test vectors. So the problem of the combinational test generation was formulated to an optimization problem. The experimental results on some standard circuits demonstrate that the algorithm have high fault coverage and short test time.