In this paper, an accurate approach for estimating SRAM dynamic stability is proposed. The conventional methods of SRAM stability estimation suffer from two major drawbacks: (1) using static failure criteria, such as SNM, which does not capture the transient and dynamic behavior of SRAM operation, and (2) using quasi-Monte-Carlo simulation, which approximates the failure distribution, resulting in large errors at the tails where the desired failure probabilities exist. These drawbacks are eliminated by employing a new distribution- independent, Most-Probable-Failure-Point search technique for accurate probability calculation along with accurate simulation-based dynamic failure criteria. Compared to previously published techniques, the proposed technique offers orders of magnitude improvement in accuracy. Moreover, the proposed technique enables the correct evaluation of stability in real operation conditions and for different dynamic circuit techniques, such as dynamic writeback, where the conventional methods are not applicable.