Analog fault diagnosis for unpowered circuit boards

Abstract

We present key portions of a method for automatic analog fault diagnosis for unpowered circuit boards. Our work consists of two major parts: (1) test point selection and (2) stimuli selection for diagnostic test generation. For test point selection, we propose an efficient graph-based algorithm achieving a desired level of diagnosibility. The stimuli selection algorithm uses a cost function derived from the sensitivity matrix to select test stimuli and thus avoids expensive circuit simulation. Experimental results of several industrial circuits show that our method is time efficient and promising in selecting high-quality stimuli.