当前位置:
X-MOL 学术
›
IEEE Trans. Semicond. Manuf.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing
IEEE Transactions on Semiconductor Manufacturing ( IF 2.7 ) Pub Date : 2020-08-01 , DOI: 10.1109/tsm.2020.2995548 Moslem Azamfar , Xiang Li , Jay Lee
IEEE Transactions on Semiconductor Manufacturing ( IF 2.7 ) Pub Date : 2020-08-01 , DOI: 10.1109/tsm.2020.2995548 Moslem Azamfar , Xiang Li , Jay Lee
Quality inspection in semiconductor manufacturing is of great importance in the modern industries. In the recent years, intelligent data-driven condition monitoring methods have been successfully developed and applied in the industrial applications. However, despite the promising condition monitoring performance, the existing methods generally assume the training and testing data are from the same distribution. In practice, due to variations in manufacturing process, the collected data are usually subject to different distributions in different operating conditions, that significantly deteriorates the performance of the data-driven methods. To address this issue, this paper proposes a deep learning-based domain adaptation method for fault diagnosis in semiconductor manufacturing. The maximum mean discrepancy metric is optimized on the learned high-level data representation in the deep neural network. Experimental results on a real-world semiconductor manufacturing dataset suggest the proposed method offers an effective and generalized data-driven fault diagnosis approach for quality inspection.
中文翻译:
基于深度学习的半导体制造故障诊断领域自适应方法
半导体制造中的质量检测在现代工业中非常重要。近年来,智能数据驱动的状态监测方法已成功开发并应用于工业应用。然而,尽管状态监测性能很有前景,但现有方法通常假设训练和测试数据来自相同的分布。在实践中,由于制造过程的变化,收集的数据在不同的操作条件下通常会受到不同的分布,这显着降低了数据驱动方法的性能。为了解决这个问题,本文提出了一种基于深度学习的域适应方法,用于半导体制造中的故障诊断。最大平均差异度量在深度神经网络中学习的高级数据表示上进行了优化。在真实世界半导体制造数据集上的实验结果表明,所提出的方法为质量检测提供了一种有效且通用的数据驱动故障诊断方法。
更新日期:2020-08-01
中文翻译:
基于深度学习的半导体制造故障诊断领域自适应方法
半导体制造中的质量检测在现代工业中非常重要。近年来,智能数据驱动的状态监测方法已成功开发并应用于工业应用。然而,尽管状态监测性能很有前景,但现有方法通常假设训练和测试数据来自相同的分布。在实践中,由于制造过程的变化,收集的数据在不同的操作条件下通常会受到不同的分布,这显着降低了数据驱动方法的性能。为了解决这个问题,本文提出了一种基于深度学习的域适应方法,用于半导体制造中的故障诊断。最大平均差异度量在深度神经网络中学习的高级数据表示上进行了优化。在真实世界半导体制造数据集上的实验结果表明,所提出的方法为质量检测提供了一种有效且通用的数据驱动故障诊断方法。