R&D: Retention Accelerated Testing for 3D QLC NAND Flash Memory, Characterization, Analysis, and Modeling

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems has published an article written by Shaoqi Yang; School of Information Science and Engineering, Shandong University, Qingdao, China, Meng Zhang; School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China, Xuepeng Zhan; School of Information Science and Engineering, Shandong University, Qingdao, China, Peng Guo; Shandong Sinochip Semiconductors Co., Ltd, P. R. China, Xiaohuan Zhao; School of Information Science and Engineering, Shandong University, Qingdao, China, Guangkuo Yang, Xinyi Guo, Jixuan Wu, School of Information Science and Engineering, Shandong University, Qingdao, China, Fei Wu, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China, and Jiezhi Chen, School of Information Science and Engineering, Shandong University, Qingdao, China.

Abstract: “Three-dimensional (3D) NAND flash memory has become quite popular and is now widely used in data centers and mobile devices due to its outstanding storage density and cost-effectiveness. Larger storage capacity is made possible by 3D quad-level cell (QLC) NAND flash memory with the charge-trap (CT) structure, which stores four bits in each cell. However, data reliability is sacrificed in exchange for greater capacity. The lifespan of data retention is crucial for non-volatile storage. Thus, an important role is played by the Arrhenius model, which is widely used for lifespan prediction and high-temperature acceleration testing. Interestingly, we discover that the conventional Arrhenius model is inaccurate after analyzing the data retention properties of 3D QLC NAND flash memory. An empirical model is proposed for changing the apparent activation energy (Ea) based on the influence of different parameters, in order to accurately predict data lifespan and perform accelerated experiments. This developed model provides a temperature-and cycle-related parameter table for Ea, which is useful for high-temperature acceleration testing examinations. Simultaneously, we observe a linear connection between the 40∘C data retention time mapping and the other temperatures. We evaluate the effects of the modified Ea model and the classic Arrhenius model with the epitaxial data and conclude that the former can reduce the error by approximately 70% to a maximum.“