A technique for extracting statistical compact model parameters using artificial neural networks (ANNs) is proposed. ANNs can model a much higher degree of nonlinearity compared to existing quadratic polynomial models and, hence, can even be used in sub-100-nm technologies to model leakage current that exponentially depends on process parameters. Existing techniques cannot be extended to handle such exponential functions. Additionally, ANNs can handle multiple input multiple output relations very effectively. The concept applied to CMOS devices improves the efficiency and accuracy of model extraction. Results from the ANN match the ones obtained from SPICE simulators within 1%.Index Terms-Backward propagation of variance (BPV), compact model, extraction, neural, statistical. I. IntroductionAs semiconductor devices scale to 20 nm and below, process variations become more significant. Circuit designers need to use statistical or Monte Carlo (MC) models to assess the impact of process variation on circuit performance. The MC model specifies statistical distributions for model parameters (such as mobility and oxide thickness) that reproduce the measured or predicted distributions of performance parameters. The classic approach to determine the model parameter distribution is a time-consuming trial and error process of picking distributions and running MC until the resulting performance parameter distributions match the measured or specified result.The extraction is sped up by building an analytic model relating performance to model parameters, and in turn analytically relating the standard deviation of performance to that of model parameters. The measured target standard deviations are then back propagated to the model parameter variations through an optimization formulation. This is known as back propagation of variance (BPV). The methodology in [1] and [2] implements BPV efficiently but it only handles nonlinear relationships that can be adequately approximated using linear and quadratic terms. Principal component analysis (PCA) techniques similar to [3], although formulated to handle correlated process parameters, provide only linear relationships. These techniques work well if the degree of nonlinearity is not too high. This includes MOSFET parameters, such as Idlin, Idsat,

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