Evolutionary algorithms for global parametric fault diagnosis in analogue integrated circuits

Abstract

Abstract. An evolutionary method for analogue integrated circuits diagnosis is presented in this paper. The method allows for global parametric faults localization at the prototype stage of life of an analogue integrated circuit. The presented method is based on the circuit under test response base and the advanced features classification. A classifier is built with the use of evolutionary algorithms, such as differential evolution and gene expression programming. As the proposed diagnosis method might be applied at the production phase there is a method for shortening the diagnosis time suggested. An evolutionary approach has been verified with the use of several exemplary circuits -an oscillator, a band-pass filter and two operational amplifiers. A comparison of the presented algorithm and two classical methods -the linear classifier and the nearest neighborhood method -proves that the heuristic approach allows for acquiring significantly better results.

Quantum Genetic Algorithm Based on Qutrits and Its Application

“…Probabilistic mechanism of the quantum computations combined with the evolutionary algorithm provides a global search for a solution with a rapid convergence and a small value of population. These algorithms have demonstrated their effectiveness for solving combinatorial and functional optimization problems, mechanical engineering optimization problems, image processing, and many others [4][5][6][7][8].…”

Quantum Genetic Algorithm on Multilevel Quantum Systems

“…The probabilistic mechanism of the quantum computations in conjunction with the evolutionary algorithm provides a global search for the solution with fast convergence and small population size. This algorithm is successfully applied to different science and engineering problems, which require approximate, close to the optimum, solutions after a minimum computation time [3][4][5][6].…”

Analog Circuit Specification Testing by Means of Walsh–Hadamard Transform and Multiple Regression Supported by Evolutionary Computations

“…In [11,19,35], there are the techniques that use support vector machines for faults detection. Heuristic computations and statistical analysis are used in [4,[6][7][8]13,18,20] for catastrophic and global parametric faults detection as well as for optimal test points and testing signal shape searching, respectively. In [34], the entropy parameter is used to analog circuit soft faults detections.…”