  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.
