Characterization of TLC 3D-NAND Flash Endurance through Machine Learning for LDPC Code Rate Optimization

The advent of the 3D-NAND Flash memories introduced significant issues in terms of characterization and system-level optimization that can be performed to increase the memory reliability over its lifetime. Indeed, the knobs that a system designer can leverage to this extent are many. In this work we show that the application of machine learning algorithms like data clustering on a large characterization data set of TLC 3D-NAND Flash devices can help the designers in optimizing the countermeasures for improving the memory reliability while reducing their implementation cost.