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In this paper, we present a unified approach for the statistical timing and leakage analysis of circuits in the presence of intradie variations. The intradie variations in device parameters are modeled as a spatial stochastic process with a given covariance function. The covariance function is used to construct a Karhunen-Loeve expansion of the spatial process. This leads to representing the various parameters of all components on the chip in terms of a common set of abstract random variables. The leakage and propagation delay of each gate are represented as quadratic polynomials (QPs), which are elements of a vector space whose bases are multivariate quadratic orthogonal polynomials of the device parameters. In the case of signal arrival times, we describe an efficient method to propagate the QPs through the circuit to obtain a QP representation of the signal arrival times at the primary outputs. The analysis is extended to include sequential components so that flip-flop parameters and clock arrival times can be treated as random variables. This allows efficient estimation of the timing yield of the circuit. We show how a similar representation of QP can be used to model leakage of gates and develop an efficient method to compute a QP representation of the total chip leakage. The proposed techniques and quadratic models were exercised on ISCAS89 benchmark circuits and compared with Monte Carlo (MC) simulations. The results show that the techniques are very accurate and several orders of magnitude faster than MC simulation.