Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets
This addresses flash memory channel modeling for storage systems, but it is incremental as it applies an existing method to a specific domain.
The paper tackled modeling spatio-temporal characteristics in NAND flash memory read voltages using conditional generative networks, achieving accurate capture of spatial and temporal features as validated by cell read voltage distributions and error rates.
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.