| Title |
The Study on Machine Learning Approach for Optimization of Superjunction MOSFET |
| Authors |
이경엽(Gyeongyeop Lee) ; 하종현(Jonghyun Ha) ; 김정식(Jungsik Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.10.1475 |
| Keywords |
Machine Learning; Superjunction MOSFET; numerical simulation; TCAD simulation |
| Abstract |
In this work, the the adoption of machine learning for optimization of superjunction MOSFET is investigated. Abundant data (on-resistance(), breakdown voltage(BV)) with various process parameters is earned by technology computer-aided design (TCAD) simulation. We also compare the prediction accuracy between eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). XGBoost shows higher accuracy than LightGBM. The use of machine learning is very effective way to reduce the cost and time of superjunction MOSFET development. |