The study on machine learning approach for optimization of superjunction MOSFET
- Lee, G.; Ha, J.; Kim, J.
- Issue Date
- Korean Institute of Electrical Engineers
- Machine Learning; Numerical simulation; Superjunction MOSFET; TCAD simulation
- Transactions of the Korean Institute of Electrical Engineers, v.70, no.10, pp.1475 - 1480
- Journal Title
- Transactions of the Korean Institute of Electrical Engineers
- Start Page
- End Page
- In this work, the the adoption of machine learning for optimization of superjunction MOSFET is investigated. Abundant data (on-resistance(RON), 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. Copyright ? The Korean Institute of Electrical Engineers.
- Files in This Item
- There are no files associated with this item.
- Appears in
- 공과대학 > 전기공학과 > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.