Code-Aware Storage Channel Modeling via Machine Learning

With the reduction in device size and the increase in cell bit-density, NAND flash memory suffers from larger inter-cell interference (ICI) and disturbance effects. Constrained coding can mitigate the ICI effects by avoiding problematic error-prone patterns, but designing powerful constrained codes requires a comprehensive understanding of the flash memory channel. Recently, we proposed a modeling approach using conditional generative networks to accurately capture the spatio-temporal characteristics of the read signals produced by arrays of flash memory cells under program/erase (P/E) cycling. In this paper, we introduce a novel machine learning framework for extending the generative modeling approach to the coded storage channel. To reduce the experimental overhead associated with collecting extensive measurements from constrained program/read data, we train the generative models via transferring knowledge from models pre-trained with pseudo-random data. This technique can accelerate the training process and improve model accuracy in reconstructing the read voltages induced by constrained input data throughout the flash memory lifetime. We analyze the quality of the model by comparing flash page bit error rates (BERs) derived from the generated and measured read voltage distributions. We envision that this machine learning framework will serve as a valuable tool in flash memory channel modeling to aid the design of stronger and more efficient coding schemes.