Dynamic Learning and On-line Prognostics System in PECVD Process

• Main Authors: Shun-Jyun Huang, 黃舜君
• Other Authors: Ming Jhang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/28253162217681567718

碩士 === 中原大學 === 機械工程研究所 === 95 === This research mainly combines artificial neural networks (ANNs) and an expert system to constitute an intelligent on-line diagnosis system that has dynamic learning function. The system is used to improve the error margin of the machine parameter which arises due to the drifting during the manufacturing process. We use active learning and re-usable training characteristics of ANNs to extract the knowledge. ANNs has learnt this knowledge by Fuzzy rule extraction method in the (IF...THEN...) form and store them in the knowledge base of the expert system. Because of the system contains ANNs, the weight of the neuron of ANNs will increase continuously with new data. After extracting the new rules, we constitute an intelligent expert system with knowledge base rule. We can provide the adjustment suggestion of the recipe by the new extracted rules and its trust value. In the part of the dynamic learning, we refer a strategy about the retraining of ANNs. We explain the phenomenon of the weight vector changed in ANNs by this strategy. We take the machine parameter in PECVD process as example to describe each step of this system in detail. We collected 1425 data of the training sample of dynamic learning in ANNs and predicted of the membrane thickness with the intelligent on-line diagnosis system, then putting forward the suggestion for recipe adjustment to the engineers. The experimental results show that the error rate in average membrane thickness measurement decreases from 10.14% to 6.64% when new training data are combined. These confirm that ANNs dynamic learning can improve the error rate of the prediction and the result of yield rate.