Loadsa: A yield-driven top-down design method for STT-RAM array

Abstract

As an emerging nonvolatile memory technology, spin-transfer torque random access memory (STT-RAM) faces great design challenges. The large device variations and the thermal-induced switching randomness of the magnetic tunneling junction (MTJ) introduce the persistent and non-persistent errors in STT-RAM operations, respectively. Modeling these statistical metrics generally require the expensive Monte-Carlo simulations on the combined magnetic-CMOS models, which is hardly integrated in the modern micro-architecture and system designs. Also, the conventional bottom-up design method incurs costly iterations in the STT-RAM design toward specific system requirement. In this work, we propose Loadsa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> : a yield-driven top-down design method to explore the design space of STT-RAM array from a statistical point of view. Both array-level semi-analytical yield model and cell-level failure-probability model are developed to enable a top-down design method: The system-level requirements, e.g., the chip yield under power and area constraints, are hierarchically mapped to array-and cell-level design parameters, e.g., redundancy, ECC scheme, and MOS transistor size, etc. Our simulation results show that Loadsa can accurately optimize the STT-RAM based on the system and cell-level constraints with a linear computation complexity. Our method demonstrates great potentials in the early design stage of memory or micro-architecture by eliminating the design integrations, while offering a full statistical view of the design even when the common yield enhancement practices are applied.