ARA-RCIV: Identifying Reliability-Critical Input Vectors of Logic Circuits Based On the Association Rules Analysis Approach

The identification of reliability-critical input vectors (RCIVs) is vital in the assessment and prediction of reliability boundaries for logic circuits. This article introduces an approach grounded in association rule analysis (ARA) to swiftly and efficiently identify RCIVs in both combinational and sequential circuits. The utilization of the ARA model for validating the circuit’s associated primary inputs enhances accuracy while simultaneously reducing the complexity of RCIVs identification. Orienting the generation of new samples with associated inputs expedites the identification process. Quantifying circuit complexity enables the adaptive assignment of algorithmic parameters to circuits of diverse sizes. The construction of input sets facilitates a precise evaluation of the reliability of individual input vectors in sequential circuits. Experimental results on benchmark circuits illustrate that this approach achieves a mean accuracy of 0.9952, with Monte Carlo (MC) method serving as the reference, for small and medium-sized circuits, and require only 20.71% of MC’s time overhead. The average coverage of 0.9884 surpasses the reference method by 1.8 times. The stability is 4.35 times higher with the random method on large scale circuits with 224,624 gates and 6,642 primary inputs. Circuit designers can swiftly ascertain the average reliability and reliability boundaries of a circuit by using this approach for RCIVs identification. By applying optimizations of the identified RCIVs to expedite convergence and mitigate fluctuations, the influence of these RCIVs can be minimized in reliability evaluation and testing.