A sensor fusion based methodology for real time furnace diagnostics

Precise control of process temperature has become increasingly important in today's semiconductor industry. Multi-zone batch furnaces are used widely in current manufacturing lines, and high reliability of furnace systems is a crucial factor in achieving high product yield. However, uncertainty caused by sensor noise and failure may degrade reliability. In this work, we developed a methodology based on thermal modeling and sensor fusion techniques to detect temperature sensor failures, power supply failures, and system faults for the multi-zone furnace systems. The typical types of failures have been defined. The impact of single failures and different combinations of failures on the system behavior has been studied. The furnace system has been modeled based on both physical considerations and experimental data extraction. The fault detection methodology has been tested in simulations. Principal component analysis (PCA) is utilized for choosing data types for different fault detection purposes. Sensor fusion is used to enhance reliability.