College of Electrical Engineering and Computer Science, National Chiao-Tung University, Hsinchu, Taiwan.
Institute of Electronics Engineering, National Chiao-Tung University, Hsinchu, Taiwan.
PLoS One. 2019 Aug 13;14(8):e0220607. doi: 10.1371/journal.pone.0220607. eCollection 2019.
While there have been many studies using machine learning (ML) algorithms to predict process outcomes and device performance in semiconductor manufacturing, the extensively developed technology computer-aided design (TCAD) physical models should play a more significant role in conjunction with ML. While TCAD models have been effective in predicting the trends of experiments, a machine learning statistical model is more capable of predicting the anomalous effects that can be dependent on the chambers, machines, fabrication environment, and specific layouts. In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure. The mixed dataset with the experimental and TCAD results improved the prediction in terms of accuracy. With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study. In this method, statistical induction and analytical deduction can be combined to increase the prediction accuracy of future intelligent semiconductor manufacturing.
虽然已经有许多研究使用机器学习 (ML) 算法来预测半导体制造中的工艺结果和设备性能，但广泛开发的技术计算机辅助设计 (TCAD) 物理模型应该与 ML 一起发挥更重要的作用。虽然 TCAD 模型在预测实验趋势方面非常有效，但机器学习统计模型更能够预测可能依赖于腔室、机器、制造环境和特定布局的异常效应。在本文中，我们使用基于 TCAD 的分析-统计混合训练 (ASMT) 方法。在这种方法下，将 TCAD 模型纳入机器学习训练过程。包含实验和 TCAD 结果的混合数据集提高了预测的准确性。通过将 ASMT 应用于 BOSCH 工艺，我们表明，在使用分析-统计混合训练 (ASMT) 方案而不是基线研究中使用的经典神经网络 (NN) 时，可以有效地降低均方误差 (MSE)。在这种方法中，可以结合统计归纳和分析演绎来提高未来智能半导体制造的预测准确性。
