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 simulations 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 defectinduced 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 45 nm technology. Preprint General Copyright NoticeThis article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. This is the author's "personal copy" of the final, accepted version of the paper published by Science China Press and Springer-Verlag Berlin Heidelberg. 1 Fraunhofer IIS/EAS, Dresden D-010169,G e r m a n y 2 University of Freiburg, Freiburg D-79110,G e r m a n y 3 University of Paderborn, Paderborn D-33098,G e r m a n y 4 University of Passau, Passau D-94023,G e r m a n y 5 University of Stuttgart, Stuttgart D-70569,G e r m a n y Received March 21, 2011; accepted June 21, 2011Abstract 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 simulations 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 defectinduced 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 i...

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