Variability in process parameters is making accurate timing analysis of nano-scale integrated circuits an extremely challenging task. In this paper, we propose a new algorithm for statistical timing analysis using Levelized Covariance Propagation (LCP). The algorithm simultaneously considers the impact of random placement of dopants (which makes every transistor in a die independent in terms of threshold voltage) and the spatial correlation of the process parameters such as channel length, transistor width and oxide thickness due to the intra-die variations. It also considers the signal correlation due to reconvergent paths in the circuit. Results on several benchmark cir