Universal statistical cure for predicting memory loss

Novel nonvolatile memory (NVM) technologies are gaining significant attention from semiconductor industry in the competition of universal memory development. However, as nanoscale devices, these emerging NVMs suffer from the intrinsic technology challenges such as large process variations. The importance of effective statistical approaches for yield estimation and robust design arises in the commercialization of the emerging nonvolatile memory technologies. In this paper, we used Spin-Transfer Torque Random Access Memory (STT-RAM) as an example to explain some new memory failures mechanisms we have to face in the emerging memory technologies. Then, we applied a mixture importance sampling methodology to enable yield-driven design and extended its application beyond memories to peripheral circuits and logic blocks. The goal of these discussions is to propose a universal statistical methodology to predict memory loss and enable robust design practices.