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.

A method of a global parametric faults diagnosis in analogue integrated circuits is presented in this paper. The method is based on basic features calculated from a circuit's under test time domain response to a voltage step, i.e. locations of maxima and minima of circuit under test response and its first order derivative. The testing and diagnosis process is executed with the use of an artificial neural network. The neural network is supplied with extracted basic features. After evaluation and discrimination, the neural network outputs indicate the circuit state. The proposed diagnosis method has been verified with the use of exemplary integrated circuits -an operation amplifier µA741 and an integrated band-pass filter.

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