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Precise control of process temperature has become increasingly important in today's semiconductor industry. Multizone 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, the authors develop a methodology based on thermal modeling and sensor fusion techniques to detect temperature sensor failures, power supply failures, and system faults for the multizone 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 is utilized for choosing data types for different fault detection purposes. Sensor fusion is used to enhance reliability. Simulation results show that all different types of failures can be detected when data are rich enough. Experimental results show that all single failures and some of the failure combinations can be estimated when only steady-state and cooling-down data are utilized.