Abstract
We propose a Rényi's entropy based analog circuit soft fault detection method. This method extracts the entropy information from the probability density function (PDF) of the output of the circuit under test (CUT), which is sensitive to the parameters of circuits. In this method, firstly, the Lagrange multiplier method with Rényi's entropy is used to deduce PDF of the output signal. Then through the maximum likelihood estimation method, we estimate the parameter α of Rényi's entropy adaptively according to the output of CUT. Finally, the value of Rényi's entropy can be calculated using the PDF and α parameter. The divergence between the Rényi's entropy corresponding to the fault and fault free circuits is adopted to detect the fault. This method can 100% detect soft faults, including the single fault and multiple faults, without complicate models and mass of data, and also with no need of interrupting the inherent contentions of CUT. Experiments are conducted respectively on two circuits that are implemented on an actual circuit board. The effectiveness of the proposed method is demonstrated by the result of the experiment.
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