Ternary-State Vertical NAND Flash Memory for the Improvement of the Density and Accuracy of Quantized Neural Networks

Novel ternary-state vertical NAND (VNAND) flash memory is proposed for high-density and high-accuracy quantized neural networks (QNNs) for the first time in this study. The proposed ternary-state VNAND features an additional current saturation region in transfer curves to implement ternary weights for QNNs using only a single memory cell. It achieves the inference accuracy of 96.06% and 81.04% for the MNIST and CIFAR-10 datasets, respectively, while maintaining high memory density. It is confirmed that the QNN accuracy of the proposed ternary-state VNAND is robust to variations in the threshold voltage ( <tex-math notation="LaTeX">Vₜₕ)</tex-math> and channel hole diameter ( <tex-math notation="LaTeX">dch)</tex-math> .