Retention Improvement in Vertical NAND Flash Memory Using 1-bit Soft Erase Scheme and its Effects on Neural Networks

Abstract

We propose a selective 1-bit soft erase scheme in vertical NAND (V-NAND) flash memory that improves retention characteristics. Selective 1-bit erase using gate-induced drain leakage (GIDL) is applied after program operation to remove shallowly trapped electrons. Compared to conventional methods, the proposed method improves retention characteristics by 40% and the distribution of Delta V-{ text {th}} is narrowed to less than 30%. The proposed V-{ text {th}} tuning scheme accurately adjusts V-{ text {th}} to the target V-{ text {th}}, thereby improving the error rate of convolutional neural networks (CNNs) for image classification by 17% by adopting the 1-bit soft erase scheme. © 2022 IEEE.