Feature Extraction from Equipment Sensor Signals with Time Series Clustering and Its Application to Defect Prediction

2020 ◽  
Author(s):  
Daisuke Hamaguchi ◽  
Tomonari Masada ◽  
Takumi Eguchi
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Defect Prediction ◽  
Time Series Clustering ◽  
Sensor Signals

scholarly journals Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3880
Author(s):  
Kyuchang Chang ◽  
Youngji Yoo ◽  
Jun-Geol Baek
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Semiconductor Manufacturing ◽  
Time Series Data ◽  
Series Data ◽  
Signal Segmentation ◽  
Sensor Signals ◽  
Two Phases ◽  
One Class Classification

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 (iF) 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.

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