Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

TL;DR

This paper introduces a data-driven model using conditional generative networks to accurately capture the spatio-temporal behavior of NAND flash memory read voltages, considering cell interactions and cycling time.

Contribution

It presents a novel approach that models flash memory channels with a learned generative network, capturing complex spatial and temporal dependencies.

Findings

Model accurately reproduces read voltage distributions

Effectively captures inter-cell interference patterns

Predicts error rates over different cycling times

Abstract

We propose a data-driven approach to modeling the spatio-temporal characteristics of NAND flash memory read voltages using conditional generative networks. The learned model reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the specified program/erase (P/E) cycling time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We conclude that the model accurately captures the spatial and temporal features of the flash memory channel.