Abstract
This paper presents a fault detection algorithm to detect parametric fault in linear and weakly non-linear analog circuits by Bhattacharyya measure, a statistical metric. Linear feedback shift register (LFSR) generated pseudo-random bit sequences are fed to digital-to-analog converter (DAC) to obtain random analog input stimuli for the circuit under test (CUT). Bhattacharyya coefficient is measured from the probability density function (PDF) of the output. The non-Gaussian auto-regressive model is used to estimate the PDF. Component tolerance is mapped into statistical space by Monte Carlo simulation. The proposed methodology is validated through three benchmark circuits: continuous-time low pass state variable filter circuit, fourth order low pass Chebyshev filter circuit and cascade amplifier. All the circuits are simulated with CADENCE Virtuoso using UMC-180nm technology. Defect screening is also measured with linear regression analysis. Detectability of the proposed method for parametric fault is reasonably large in comparison to functional test method.
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