Ma Hao, Duan Xiaoling, Wang Shulong, Liu Shijie, Zhang Jincheng, Hao Yue
School of Microelectronics, Xidian University, Xi'an 710071, China.
Micromachines (Basel). 2023 Jan 12;14(1):188. doi: 10.3390/mi14010188.
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.
氮化镓结型势垒肖特基（JBS）二极管在电力电子领域表现出优异的性能。然而，器件性能受P+区多个参数的影响，传统的TCAD模拟方法复杂且耗时。在本研究中，我们使用神经网络机器学习方法来预测氮化镓JBS二极管的性能。首先，使用TCAD工具获得了由器件结构和性能参数组成的3018组样本数据。然后将这些数据输入到已建立的神经网络中进行训练，该网络能够快速预测器件性能。最终预测结果表明，导通电阻和反向击穿电压的平均相对误差分别为0.048和0.028。预测值具有良好的拟合效果。该方法能够快速设计出具有目标性能的氮化镓JBS二极管，并加速氮化镓JBS二极管性能预测的研究。
