Real-time monitoring of complex sensor data using wavelet-based multiresolution analysis

Advancements made in sensor technology have resulted in complex sensor data that captures multiple process events. Real-time monitoring of complex manufacturing processes, such as nano-scale semiconductor polishing, often requires analysis of such complex sensor data. The multiple events in a process occur at multiple scales or frequencies (also referred to as multiscale) and are localized at different points in time. Recent literature contains several wavelet decomposition based multiscale sensor data analysis techniques including those that are developed for process monitoring applications, such as tool-life monitoring, bearing defect monitoring, and monitoring of ultra-precision processes. However, most of the above mentioned wavelet-based sensor data analysis techniques are designed for offline implementation. In an offline method, one can perform wavelet decomposition of longer data lengths in order to capture information needed for monitoring. However, this is computationally involved and needs longer processing time, which becomes a serious challenge in online (real time) applications. This paper first presents a complete online multiscale process monitoring methodology. The methodology is designed to deal with real-time analysis and testing of very high rate of process data collected by sensors. This is particularly critical and becomes a challenge for high rate of data collection by the sensors which pose additional difficulty of matching data processing rate with the data acquisition rate. The methodology is capable of displaying the analysis results through real time graphs for ease of process supervisory decision making. The methodology is demonstrated via a nano-scale silicon wafer polishing application. Sufficient details of the application are provided to assist readers in implementing this methodology for other processes. The results show that the methodology has the ability to deal with high rate of data collection as well as multiscale event detection.