Visual reverse engineering using SPNs for automated testing and diagnosis of digital circuits

This paper presents a new methodology, which leads to the development of a prototype system for the automated testing and faults diagnosis of digital circuits. The methodology presented here is based on an automated visual reverse engineering process, which assists the extraction of the functional behavior of a digital circuit and for the detection and diagnosis of faults and defects on it. The visual information extracted from a PCB (printed circuit board) or a digital circuit is represented by SPN (stochastic Petri-net) forms in order to maintain both its structural and functional characteristics.