A SIFT-based Waveform Clustering Method for aiding analog/mixed-signal IC Verification

This paper proposes a method for speeding-up the verification process of integrated circuits, featuring waveform clustering of circuit response signals. The main objective is to automatically separate the signals into distinct groups that potentially exhibit visual similarities in order to aid the visual inspection/verification. As a first step, the proposed method extracts SIFT-like features by finding stable points of the signal over the scale space and computing robust descriptors able to describe their neighborhood. The resulted descriptors are quantized in order to be used in the clustering process as bag-of-words histograms. We demonstrate the validity of our method on a circuit waveform database containing several thousands of signals belonging to ten electrical tests.