A Statistical Approach of Analog Circuit Fault Detection Utilizing Kolmogorov–Smirnov Test Method

This work presents a testing technique based on ‘Kolmogorov–Smirnov’ (K–S) test for detection of parametric faults in analog circuits. The proposed method is a time-domain signal processing technique that compares the statistical similarity in terms of ‘Empirical Cumulative Distribution Function’ (ECDF) of the outputs of the circuit when the input of the circuit is a random analog signal. ‘Multivariate Adaptive Regression Splines’ (MARS) technique is used to map the tolerances of functional metrics to the components of the circuit under test (CUT). Two benchmark circuits, i.e., second-order Sallen–Key band-pass filter and weakly nonlinear cascade amplifier are tested to validate the proposed fault detection technique. The proposed statistical approach with the use of random analog signal as input excitation results reduction of complexity for designing input test signal and increases fault coverage in the analog circuit testing. The proposed method is testified experimentally for Sallen–Key band-pass filter. The experimental results are in good agreement with the simulated results.