Continuous equipment diagnosis using evidence integration: an LPCVD application

A diagnostic system that employs the Dempster-Shafer (D-S) evidential reasoning technique to conduct malfunction diagnosis on semiconductor manufacturing equipment has been developed. This is accomplished by combining the continuous stream of information that originates from maintenance status records, from real-time sensor measurements, and from the differences between inline measurements and values predicted by equipment models. Using this information, equipment malfunctions are analyzed and their causes are inferred through the resolution of qualitative and quantitative constraints. The qualitative constraints describe the normal operation of the equipment. The quantitative constraints are numerical models that apply to the manufacturing step in question. These models are specifically created and characterized through experimentation and statistical analysis, and they can be updated to reflect equipment aging. The violation of these constraints is linked to the evaluation of continuous belief functions for the calculation of the belief associated with the various types of failure. The belief functions encapsulate the experience of many equipment maintenance specialists. Once created, the belief functions can be fine-tuned automatically, drawing from historical maintenance records. These records are stored in symbolic form to facilitate this task, and they must be updated to track equipment changes over time. A prototype of this diagnostic system was implemented in an object-oriented programming environment. The effectiveness of this technique was demonstrated on a low-pressure chemical vapor deposition (LPCVD) reactor used for the deposition of undoped polysilicon