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We propose a data-driven approach to modeling the spatio-temporalcharacteristics of NAND flash memory read voltages using conditional generativenetworks. The learned model reconstructs read voltages from an individualmemory 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 evaluatethe model over a range of time stamps using the cell read voltagedistributions, the cell level error rates, and the relative frequency of errorsfor patterns most susceptible to inter-cell interference (ICI) effects. Weconclude that the model accurately captures the spatial and temporal featuresof the flash memory channel.

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