| Title |
Prediction of Degradation Characteristics in Saddle Fin DRAM Due to Total Ionizing Dose and Displacement Defect using Deep Neural Network |
| Authors |
류민상(Minsang Ryu) ; 하종현(Jonghyeon Ha) ; 이경엽(Gyeongyeop Lee) ; 서민기(Minki Suh) ; 방민지(Minji Bang) ; 이다복(Dabok Lee) ; 김정식(Jungsik Kim) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.11.29 |
| Keywords |
Machine learning; DNN; Saddle fin DRAM; TCAD; TID; DD |
| Abstract |
In this paper, the effects of total ionizing dose (TID) and displacement defect (DD) in saddle fin dynamic random access memory (DRAM) are investigated using technology computer-aided design (TCAD) simulation and deep neural network (DNN). TCAD is used for generating the current-voltage characteristic data of the saddle fin DRAM and the energy level, concentration, location, and area of the trap are utilized for variables. The TCAD dataset is divided into preprocessed and un-processed cases to compare the prediction accuracy of DNN. The result shows that the model trained with preprocessing has an 80 % increase in mean square error (MSE) loss and a 37 % increase in R2 score compared to the training model without preprocessing. Therefore, preprocessing of a dataset is necessary for high prediction accuracy using DNN. |