Identifying resistive open defects in embedded cells under variations

Abstract

Small Delay Faults (SDFs) due to weak defects and marginalities have to be distinguished from extra delays due to process variations, since they may form a reliability threat even if the resulting timing is within the specification. In this paper, it is shown that these faults can still be identified, even if the corresponding defect cell is deeply embedded into a combinational circuit and its observability is restricted. The results of a few delay tests at different voltages and frequencies serve as the input to machine learning procedures which can classify a circuit as marginal due to defects or just slow due to variations. Several machine learning techniques are investigated and compared with respect to accuracy, precision, and recall for different circuit sizes and defect scales. The classification strategies are powerful enough to sort out defective devices without a major impact on yield.