| Title |
Deep Learning Approach for Electrical Characteristics Analysis of 3D Vertical SONOS NAND FLASH by Grain Boundary Distribution and Geometrical Variation |
| Authors |
하종현(Jonghyeon Ha) ; 방민지(Minji Bang) ; 이다복(Dabok Lee) ; 서민기(Minki Suh) ; 류민상(Minsang Ryu) ; 김정식(Jungsik Kim) |
| DOI |
https://doi.org/10.5573/ieie.2024.61.4.13 |
| Keywords |
Machine learning; DNN; 3D vertical SONOS NAND; TCAD; Geometrical variability |
| Abstract |
3D Vertical Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) NAND was developed as a solution to the scaling down of planar-type NAND Flash memory. With 3D Vertical SONOS NAND, it is possible to stack many transistors on a limited-size wafer to secure more memory capacity than the planar type. However, with the change to the vertical structure, the cost of the process has increased along with the increase in process difficulty. Therefore, there is a need for a technology that reduces the process's cost and predicts the device's electrical characteristics quickly and accurately. In this paper, we used TCAD simulation and deep learning to predict and analyze the variation of electrical characteristics (Vtgm and Vti) of 3D Vertical SONOS NAND due to polysilicon grain boundary distribution (Max-angle, Ycut, Xseed, Yseed, Aseed) and geometrical variation (Width, Lcg). The electrical characteristics were predicted using deep learning trained based on TCAD simulation results and converged to TCAD simulation results with very high R2 scores (Vtgm R2 score = 0.997, Vti R2 score = 0.999). We also evaluated the importance of the input parameters through the SHapley Additive exPlanations (SHAP) value. We found that the Ycut and Xseed had the most influence on the variation of electrical characteristics. |