Variability-Aware Machine Learning Strategy for 3-D NAND Flash Memories
Kyul Ko, Jang-Kyu Lee, Hyungcheol Shin
Abstract
This article proposes a variability-aware machine learning (ML) approach that predicts variations in the key electrical parameters of 3-D NAND Flash memories. For the first time, we have verified the accuracy, efficiency, and generality of the predictive impact factor effects of artificial neural network (ANN) algorithm-based ML systems. ANN-based ML algorithms can be very effective in multiple-input and multiple-output (MIMO) predictions. Therefore, changes in the key electrical characteristics of the device caused by various sources of variability are simultaneously and integrally predicted. This algorithm benchmarks 3-D stochastic TCAD simulation, showing a prediction error rate of less than 1%, as well as a calculation cost reduction of over 80%. In addition, the generality of the algorithm is confirmed by predicting the operating characteristics of the 3-D NAND Flash memory with various structural conditions as the number of layers increases.