Department of Industrial and Management Engineering, Korea University, Seoul 02841, Korea.
Samsung Electronics Co., Ltd., Hwaseong-si 18448, Korea.
Sensors (Basel). 2021 Jun 4;21(11):3880. doi: 10.3390/s21113880.
This paper proposes a new diagnostic method for sensor signals collected during semiconductor manufacturing. These signals provide important information for predicting the quality and yield of the finished product. Much of the data gathered during this process is time series data for fault detection and classification (FDC) in real time. This means that time series classification (TSC) must be performed during fabrication. With advances in semiconductor manufacturing, the distinction between normal and abnormal data has become increasingly significant as new challenges arise in their identification. One challenge is that an extremely high FDC performance is required, which directly impacts productivity and yield. However, general classification algorithms can have difficulty separating normal and abnormal data because of subtle differences. Another challenge is that the frequency of abnormal data is remarkably low. Hence, engineers can use only normal data to develop their models. This study presents a method that overcomes these problems and improves the FDC performance; it consists of two phases. Phase I has three steps: signal segmentation, feature extraction based on local outlier factors (LOF), and one-class classification (OCC) modeling using the isolation forest () algorithm. Phase II, the test stage, consists of three steps: signal segmentation, feature extraction, and anomaly detection. The performance of the proposed method is superior to that of other baseline methods.
本文提出了一种新的半导体制造过程中传感器信号的诊断方法。这些信号为预测成品的质量和产量提供了重要信息。在这个过程中收集的大部分数据是用于实时故障检测和分类（FDC）的时间序列数据。这意味着在制造过程中必须进行时间序列分类（TSC）。随着半导体制造技术的进步，正常数据和异常数据之间的区别变得越来越重要，因为在识别过程中出现了新的挑战。一个挑战是需要极高的 FDC 性能，这直接影响到生产力和产量。然而，由于细微的差异，一般的分类算法可能难以分离正常数据和异常数据。另一个挑战是异常数据的频率非常低。因此，工程师只能使用正常数据来开发模型。本研究提出了一种克服这些问题并提高 FDC 性能的方法；它由两个阶段组成。第一阶段有三个步骤：信号分段、基于局部离群因子（LOF）的特征提取和使用隔离森林（）算法的单类分类（OCC）建模。第二阶段，即测试阶段，包括三个步骤：信号分段、特征提取和异常检测。所提出的方法的性能优于其他基线方法。
