Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks

Abstract

The breakdown performance is a critical metric for power device design. This paper explores the feasibility of efficiently predicting the breakdown performance of silicon on insulator (SOI) lateral power device using multi-layer neural networks as an alternative to expensive technology computer-aided design (TCAD) simulation. In this work, we propose the first breakdown performance prediction framework, PowerNet, for SOI lateral power devices, based on deep learning methods. The framework can provide breakdown location prediction and breakdown voltage (BV) prediction by utilizing a two-stage machine learning method. In addition, it demonstrates 97.67% accuracy on breakdown location prediction and less than 4% average error on the BV prediction compared with TCAD simulation. The proposed method can be used to measure changes in performance caused by random variability in structural parameters during manufacturing process, allowing designers to avoid unstable structural parameters and enhance design robustness. More importantly, it can significantly reduce the computational cost when compared with the TCAD simulation. We believe the proposed machine learning technique can significantly speedup the design space exploration for power devices, eventually reducing the overall product-to-market time.

Keywords

• Breakdown location
• breakdown voltage
• deep neural networks (DNN)
• Gaussian process regression (GPR)
• SOI lateral power device