DOAJ Open Access 2023

GaN JBS Diode Device Performance Prediction Method Based on Neural Network

Hao Ma Xiaoling Duan Shulong Wang Shijie Liu Jincheng Zhang +1 lainnya

Abstrak

GaN JBS diodes exhibit excellent performance in power electronics. However, device performance is affected by multiple parameters of the P+ region, and the traditional TCAD simulation method is complex and time-consuming. In this study, we used a neural network machine learning method to predict the performance of a GaN JBS diode. First, 3018 groups of sample data composed of device structure and performance parameters were obtained using TCAD tools. The data were then input into the established neural network for training, which could quickly predict the device performance. The final prediction results show that the mean relative errors of the on-state resistance and reverse breakdown voltage are 0.048 and 0.028, respectively. The predicted value has an excellent fitting effect. This method can quickly design GaN JBS diodes with target performance and accelerate research on GaN JBS diode performance prediction.

Topik & Kata Kunci

Mechanical engineering and machinery

Penulis (6)

Hao Ma

Xiaoling Duan

Shulong Wang

Shijie Liu

Jincheng Zhang

Yue Hao

Format Sitasi

APA MLA BibTeX

Ma, H., Duan, X., Wang, S., Liu, S., Zhang, J., Hao, Y. (2023). GaN JBS Diode Device Performance Prediction Method Based on Neural Network. https://doi.org/10.3390/mi14010188

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/mi14010188
Akses: Open Access ✓