Predicting ${X}$ -Sensitivity of Circuit-Inputs on Test-Coverage: A Machine-Learning Approach

Digital circuits are often prone to suffer from uncertain timing, inadequate sensor feedback, limited controllability of past states or inability of initializing memory-banks, and erroneous behavior of analog-to-digital converters, which may produce an unknown (<inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>) logic value at various circuit nodes. Additionally, many design bugs that are identified during the post-silicon validation phase manifest themselves as <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-values. The presence of such <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources on certain primary or secondary inputs of a logic circuit may cause loss of fault-coverage of a test set, which, in turn, may impact its reliability and robustness. In this paper, we provide a mechanism for predicting the sensitivity of <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources in terms of loss of fault-coverage, on the basis of learning only a few structural features of the circuit that are easy to extract from the netlist. We show that the <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources can be graded satisfactorily according to their sensitivity using support vector regression, thereby obviating the need for costly explicit simulation. Experimental results on several benchmark circuits demonstrate the efficacy, speed, and accuracy of prediction.

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