| Title |
Study of Geometry Parameter Variation in Nanosheet Field Effect Transistor using Machine-learning Methods |
| Authors |
박건호(Geonho Park) ; 김정식(Jungsik Kim) |
| DOI |
https://doi.org/10.5573/ieie.2021.58.8.43 |
| Keywords |
Nanosheet field effect transistor; Numerical simuation; T-CAD simulation; Machine learning; XGBoost |
| Abstract |
In this paper, a gradient boosting model is used to analyze the impact of parameter changes in nanosheet field effect transistor (NSFET). The data is extracted by a technical-computer aided design (T-CAD) simulation to learn machine-learning. Among the gradient tree boosting models, the accuracy of XGBoost and LightGBM (LGBM) algorithm were compared, and the importance was also analyzed. Through the combination of T-CAD simulation and machine-learning, the time and cost should be saved for development of semiconductor devices |