Analysis on Process Variation Effect of 3D NAND Flash Memory Cell through Machine Learning Model

TLDR: This work investigated process variation effect of 3D NAND flash memory cell, especially about geometric variation using a machine learning (ML) model, which has multi-input and multi-output (MIMO) structure and deep hidden layers to train and predict complex data of process variation.

Abstract: We investigated process variation effect of 3D NAND flash memory cell, especially about geometric variation using a machine learning (ML) model. Geometric variability sources impact on variation of device's electrical parameters such as threshold voltage $(\mathbf{V}_{\mathbf{t}})$ , subthreshold swing (SS), transconductance $(\mathbf{g}_{\mathbf{m}})$ and on-current $(\mathbf{I}_{\mathbf{on}})$ . All these data were analyzed with 3D stochastic Technology Computer-Aided Design (TCAD) simulation and trained through ML model, which is composed of artificial neural network (ANN). The model has multi-input and multi-output (MIMO) structure and deep hidden layers to train and predict complex data of process variation. In order to make ML model more accurate, simulation for constructing training data set was carried out with a large number of random unit cells, which are cut from various strings. The completed ML model was tested with random test data set which had not been used for training to prove its accuracy. Through the test process, ML model showed the error of up to 5% and proved the accuracy of prediction.