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Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT.
Kim, Ki Yeong; Hwang, Tae Hyun; Song, Young Suh; Kim, Hyunwoo; Kim, Jang Hyun.
Afiliação
  • Kim KY; Department of Electrical Engineering, Pukyong National University, Busan 48513, Republic of Korea.
  • Hwang TH; Department of Electrical Engineering, Pukyong National University, Busan 48513, Republic of Korea.
  • Song YS; Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea.
  • Kim H; School of Electronic and Electrical Engineering, Hankyong National University, Anseong 17579, Republic of Korea.
  • Kim JH; Department of Electrical Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Micromachines (Basel) ; 14(2)2023 Jan 28.
Article em En | MEDLINE | ID: mdl-36838033
ABSTRACT
An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss (Eoff) and on-state voltage (Von). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution. Compared to the conventional Technology Computer-Aided Design (TCAD) simulation tool, it is demonstrated that incorporating the ML algorithm into the device analysis could make it possible to achieve high accuracy and significantly shorten the simulation time. Specifically, utilizing the ML algorithm could achieve coefficients of determination (R2) of Von and Eoff of 0.995 and 0.968, respectively. In addition, it enables the optimized design to fit the target characteristics. In this study, the structure proposed for the trade-off improvement was targeted to obtain the minimum Eoff at the same Von, especially by adjusting the concentration of the separated buffer. We could improve Eoff by 36.2% by optimizing the structure, which was expected to be improved by 24.7% using the ML approach. In another way, it is possible to inversely design four types of structures with characteristics close to the target characteristics (Eoff = 1.64 µJ, Von = 1.38 V). The proposed method of incorporating machine learning into device analysis is expected to be very strategic, especially for power electronics analysis (where the transistor size is comparatively large and requires significant computation). In summary, we improved the trade-off using a separated buffer, and ML enabled optimization and a more precise design, as well as reverse engineering.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Micromachines (Basel) Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Micromachines (Basel) Ano de publicação: 2023 Tipo de documento: Article