A Reinforcement-Learning Based Approach for Designing High-Voltage SiC MOSFET Guard Rings

For high-power silicon carbide (SiC) devices, breakdown voltage analysis is an important parameter, especially for guard ring design. This work explores the implementation of machine learning on SiC guard ring parameters such as ion implanted dose and energy. In this work, the reinforcement learning...

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Bibliographic Details
Published in:IEEE open journal of power electronics 2024, Vol.5, p.1853-1861
Main Authors: Rawat, Tejender Singh, Hung, Chia-Lung, Hsiao, Yi-Kai, Yu, Wei-Chen, Elangovan, Surya, Lin, Wei-Ting, Lin, Yi-Rong, Yang, Kai-Lin, Jan, Nien-Yi, Li, Yung-Hui, Kuo, Hao-Chung
Format: Article
Language:English
Subjects:
Breakdown voltage
Computational modeling
machine learning
Network architecture
Neurons
Optimization
power device
reinforcement learning
Reliability engineering
Robustness
Scalability
SiC guard ring design
Silicon carbide
Technological innovation
Training
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Summary:For high-power silicon carbide (SiC) devices, breakdown voltage analysis is an important parameter, especially for guard ring design. This work explores the implementation of machine learning on SiC guard ring parameters such as ion implanted dose and energy. In this work, the reinforcement learning method has been successfully implemented on the 1.7 kV SiC guard ring device TCAD simulated data for the prediction of parameters. Our work has predicted the parameters successfully for the 2.5 kV guard ring design. For training, proximal policy optimization (PPO) and advantage actor-critic (A2C) RL agents were deployed. The network architecture was kept at "auto" with 3 hidden layers of 128 neurons in each layer. Our method is practically feasible and easily implemented as compared to other works, and has been shown in this paper. By using the limited design parameters of the 1.7 kV guard ring device, the trained agent has successfully predicted the design parameters for the 2.5 kV guard ring device, which has been confirmed using TCAD simulations. This work is more accurate, practical, and result-oriented, and we believe that this can significantly minimize the computational cost as compared to the standalone TCAD simulations. Also, this implementation of ML on TCAD data can substantially accelerate the design exploration for the power devices and ultimately lower product-to-market time.
ISSN:2644-1314
2644-1314
DOI:10.1109/OJPEL.2024.3496865