A Test Generation Method of R-2R Digital-to-Analog Converters Based on Genetic Algorithm

A novel multidimensional fitness function genetic algorithm is proposed to optimize test vectors of R-2R Digital-to-Analog Converters (DAC). The proposed method employs distribution of characteristic vectors and the number of test vectors to formulate a multidimensional fitness function to search a non-dominate (ND) solution set. The searching process is directed by a ND sort method. Each individual in the ND set does not contain redundant test vectors. The test vectors are taken as the input excitation of the circuit under test (CUT) and the fault diagnosis is performed. As the number of test vectors increases, the accuracy of fault diagnosis also increases. The validity of the proposed method is verified by fault diagnosis. The average fault diagnosis rate is more than 85% for the R-2R network under the influence of tolerance. In addition, the parametric faults of \(\pm 50\mathbf{\%}\) deviation from the nominal value in each resistor of the R-2R network can be detected in the proposed method. Finally, the comparative experiments and results are briefly described in this paper.