Segmentation of Multi-State Compound Waveform and Extraction of Features for Anomaly Detection

In a semiconductor fabrication plant, various types of sensors are installed at various equipment of various processes to monitor the quality of the products and the progress of various processes. These sensors generate a huge volume of time-series waveform data, which are used for failure prognostics, failure diagnosis, and anomaly detection using supervised or unsupervised classifiers. For this purpose, automatic extraction of features from a huge volume of waveform data is needed; however, extraction of effective features automatically from a compound waveform having multiple spikes, complex states or transitions is difficult. If a compound waveform is properly segmented into multiple sub-waveforms, effective features from sub-waveforms can be extracted. In this paper, we propose a new waveform segmentation method based on two-step state and change-point detection using Kernel Density Estimation (KDE) and clustering techniques and apply IEEE Standard-based methods with our state determination technique to extract features from sub-waveforms. We apply our proposed method to waveform data of real sensors installed at one of our semiconductor fabrication plants and compare the performance with the conventional step-based segmentation technique and another pattern matching technique. The experimental results show that our proposed method enables extraction of effective features, which result in higher accuracy of anomaly detection compared to conventional techniques.