Variation-Aware Fault Modeling

To achieve a high product quality for nano-scale systems both realistic defect mechanisms and process variations must be taken into account. While existing approaches for variation-aware digital testing either restrict themselves to special classes of defects or assume given probability distributions to model variabilities, the proposed approach combines defect-oriented testing with statistical library characterization. It uses Monte Carlo simu-lations at electrical level to extract delay distributions of cells in the presence of defects and for the defect-free case. This allows distinguishing the effects of process variations on the cell delay from defect-induced cell delays under process variations. To provide a suitable interface for test algorithms at higher levels of abstraction the distributions are represented as histograms and stored in a histogram data base (HDB). Thus, the computationally expensive defect analysis needs to be performed only once as a preprocessing step for library characterization, and statistical test algorithms do not require any low level information beyond the HDB. The generation of the HDB is demonstrated for primitive cells in 45nm technology.