Mechanical Engineering Commons^™

Dirichlet Process Gaussian Mixture Models For Real-Time Monitoring And Their Application To Chemical Mechanical Planarization, Jia (Peter) Liu, Omer F. Beyca, Prahalad K. Rao, Zhenyu (James) Kong, Satish T. S. Bukkapatnam

Department of Mechanical and Materials Engineering: Faculty Publications

The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent hierarchical DP clustering model for identification of specific process anomalies. The presented DPGM models are validated using numerical simulation studies as well as wireless vibration signals acquired from an experimental semiconductor chemical mechanical planarization (CMP) test bed. Through these numerically simulated and experimental sensor data, we …