Kernel Density Estimation for Optimal Detection in All-Bit Mlc Flash Memories

Abstract

NAND flash memories have recently become the main component of large-scale non-volatile storage systems. Recent studies have shown that various error sources degrade the Multi-level cell (MLC) memory performance, including intercell interference, retention error, and random telegraph noise. Accurate integration of these error sources into the analytical model to optimally derive the governing probability distributions and consequently the detection thresholds to minimize error rates lie at the heart of MLC research. Utilizing static derivations will not address the detection problem, as aforementioned error sources exhibit a strong non-stationary behavior. In this paper, a novel low-complexity implementation of a non-parametric learning mechanism, kernel density estimation, shall be used to periodically estimate the underlying probability distributions and hence approximate the optimal detection performance for time-varying all-bit-line MLC flash channel.