| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 91 === The purpose of this research is to develop a Back-propagation neural network-based (BPNN) run-to-run real-time process controller for the common process disturbances in semiconductor industries. We consider using BPNN for controlling the process disturbances, including the process noises, shifts and drifts. The performances of control result were compared with the EWMA controllers. On the other hand, we compared the control speed between the controllers and considered the MISO (multiple input single output) system for the follow-up research.
After the study, we conclude that the BPNN controller outperform the EWMA controllers in the different process disturbances. The BPNN controller has higher stability and real-time control ability. The MISO system promotes the process estimation and prediction efficiently, and the demonstration of the BPNN controller performance can arise the application and feasibility in the practical process.
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