Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks
Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks
复制标题
Powernet：利用深度神经网络进行 SOI 横向功率器件击穿预测
DOI：
10.1109/access.2020.2970966
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Pan, David Z.
中科院分区：
文献类型：
--
作者：
Chen, Jing;Alawieh, Mohamed Baker;Pan, David Z.
击穿性能是功率器件设计的一个关键指标。本文探讨了使用多层神经网络有效预测绝缘体上硅 (SOI) 横向功率器件击穿性能的可行性，作为昂贵技术计算机辅助设计 (TCAD) 仿真的替代方案。在这项工作中，我们提出了第一个基于深度学习方法的 SOI 横向功率器件击穿性能预测框架 PowerNet。该框架可以利用两阶段机器学习方法提供击穿位置预测和击穿电压（BV）预测。此外，与 TCAD 仿真相比，其故障位置预测准确率为 97.67%，BV 预测平均误差低于 4%。所提出的方法可用于测量制造过程中结构参数随机变化引起的性能变化，使设计人员能够避免不稳定的结构参数并增强设计的鲁棒性。更重要的是，与TCAD仿真相比，它可以显着降低计算成本。我们相信所提出的机器学习技术可以显着加快功率器件的设计空间探索，最终缩短整体产品上市时间。
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.