As Fin Field Effect Transistor (FinFET) scales aggressively, even a single point defect becomes a source of performance variability. The point defect is inevitably introduced not only by process damage such as epitaxial growth and ion implantation but also by cosmic rays. Technology computer-aided design (TCAD) is able to simulate the characteristics of the device with the defect. In this work, a machine learning algorithm is tested if it can reproduce the TCAD results. The impact of point defect in bulk FinFET is used as test vehicle to validate the machine-learning algorithm. TCAD is used first to generate a massive number of current-voltage characteristics dataset. The TCAD dataset is then exclusively divided into groups for machine learning training, validation and test. The trained model provides high accuracy test results within 1 % error, showing the possibility to expedite failure analysis cycle via machine learning.

中文翻译：

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用于预测 FinFET 中点缺陷效应的机器学习方法

随着鳍式场效应晶体管 (FinFET) 的快速扩展，即使是单点缺陷也会成为性能可变性的来源。点缺陷不仅不可避免地由外延生长和离子注入等工艺损伤引入，而且由宇宙射线引入。技术计算机辅助设计 (TCAD) 能够模拟具有缺陷的设备的特性。在这项工作中，测试了机器学习算法是否可以重现 TCAD 结果。散装 FinFET 中点缺陷的影响被用作验证机器学习算法的测试工具。TCAD首先用于生成大量的电流电压特性数据集。然后将 TCAD 数据集专门分为几组用于机器学习训练、验证和测试。经过训练的模型可提供 1% 误差内的高精度测试结果，