Real-time diagnosis of semiconductor manufacturing equipment using neural networks

This paper presents a tool for the real-time diagnosis of integrated circuit fabrication equipment. The approach focuses on integrating neural networks into a knowledge-based expert system. The system employs evidential reasoning to identify malfunctions by combining evidence originating from equipment maintenance history, on-line sensor data, and in-line past-process measurements. Neural networks are used in the maintenance phase of diagnosis to approximate the functional form of the failure history distribution of each component. Predicted failure rates are then converted to belief levels. For on-line diagnosis in the case of previously unencountered faults, a CUSUM control chart is implemented on real sensor data to detect very small process shifts and their trends. For the known fault case, hypothesis resting on the statistical mean and variance of the sensor data is performed to search for similar data patterns and assign belief levels. Finally, neural process models of process figures of merit (such as etch uniformity) derived from prior experimentation are used to analyze the in-line measurements, and identify the most suitable candidate among faulty input parameters (such as gas flow) to explain process shifts. A working prototype for this hybrid diagnostic system is being implemented on the Plasma Therm 700 series reactive ion etcher located in the Georgia Tech Microelectronic Research Center.