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Block based statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated due to global source of variations and path reconvergence. To the best of our knowledge, no good solution is available handling both types of correlations simultaneously. In this paper, we present a novel statistical timing algorithm, AMECT (asymptotic MAX/MIN approximation & extended canonical timing model), that produces accurate timing estimation by handling both types of correlations simultaneously. An extended canonical timing model is developed to evaluate and decompose correlations between arbitrary timing variables. And an intelligent pruning method is designed enabling trade-off runtime with accuracy. Tested with ISCAS benchmark suites, AMECT shows both high accuracy and high performance compared with Monte Carlo simulation results: with distribution estimation error < 1.5% while with around 350× speed up on a circuit with 5355 gates.