Cascaded Channel Model, Analysis, and Hybrid Decoding for Spin-Torque Transfer Magnetic Random Access Memory

Abstract

Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising non-volatile memory technology widely considered to replace dynamic random access memory (DRAM). However, there still exist critical technical challenges to be tackled. For example, process variation and thermal fluctuation may lead to both write errors and read errors, severely affecting the reliability of the memory array. In this paper, we first propose a novel cascaded channel model for STT-MRAM that facilitates fast error rate simulations and more importantly the theoretical design and analysis of memory sensing and channel coding schemes. We analyze the raw bit error rate and probabilities of dominant error events, and derive the maximum likelihood decision criterion and the log-likelihood ratio of the cascaded channel. Based on these works, we further propose a two-stage hybrid decoding algorithm for extended Hamming codes for STT-MRAM. Simulation results demonstrate the effectiveness of the proposed hybrid decoding algorithm for improving the reliability of STT-MRAM in the presence of both write errors and read errors and supporting its potential applications, such as replacing DRAM.