Accelerating Cell-Aware Model Generation for Sequential Cells using Graph Theory

The Cell-Aware (CA) methodology has become essential to detect and diagnose manufacturing intra-cell defects in modern semiconductor technologies. It characterizes standard cells by creating a defect-detection matrix, which serves as a reference that maps stimuli to the specific defects they can detect. Its limitation is that CA approach needs a number of time-consuming analog simulations to create the matrix. In [1] a graph-based methodology able to reduce the number of simulations to perform, called Transistor Undetectable Defect eLiminator (TrUnDeL), was presented. TrUnDeL can identify undetectable stimulus/defect pairs that are then excluded from the analog simulations. However, its use is limited to combinational cells and does not offer any guidance on handling sequential cells, which are usually the most complex cells. In this paper we present a new version of TrUnDeL that supports sequential cells analysis. Experiments conducted on sequential cells from two standard cell industrial libraries demonstrate that the CA generation time is reduced by 30% without compromising accuracy.