TCAD Augmented Machine Learning for Semiconductor Device Failure Troubleshooting and Reverse Engineering

Y. S. Bankapalli, H. Wong
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引用次数: 31

Abstract

In this paper, we show the possibility of using Technology Computer Aided Design (TCAD) to assist machine learning for semiconductor device failure trouble shooting and device reverse engineering. When TCAD simulation models and parameters are properly chosen and calibrated, large number of devices with random defects and structural characteristics can be generated and simulated. The results can then be used to train machine learning algorithms to predict the defect and structural characteristics of a device with given electrical characteristics (such as IV’s and CV’s). 1D PIN diode with various layer thicknesses and doping concentrations are used in this study. It is showed that with less than 2000 training samples, by using simple linear regression, one can achieve good prediction of layer thickness and doping of a given IV curve.
TCAD增强机器学习在半导体器件故障排除和逆向工程中的应用
在本文中,我们展示了使用技术计算机辅助设计(TCAD)来协助半导体器件故障故障排除和器件逆向工程的机器学习的可能性。通过正确选择和标定TCAD仿真模型和参数,可以生成和模拟大量具有随机缺陷和结构特征的器件。然后,结果可用于训练机器学习算法,以预测具有给定电特性(如IV和CV)的设备的缺陷和结构特征。本研究采用了不同层厚和掺杂浓度的一维PIN二极管。结果表明,在小于2000个训练样本的情况下,通过简单的线性回归,可以很好地预测给定IV曲线的层厚和掺杂情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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