Applying Artificial Neural Networks and Fuzzy Clustering Method for Fault Detection of CVD Equipment

• Main Authors: Chia-Hsien Ko, 柯佳賢
• Other Authors: Justin Chang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/10829491442899454915

碩士 === 中原大學 === 機械工程研究所 === 98 === Currently larger panels are manufactured in the TFT-LCD (Thin Film Transistor-Liquid Crystal Display) fabs and, moreover, the semiconductor manufacturing technology, following Moore's Laws, is developed for reduced feature size, larger wafer size, and higher device integration. As the fabrication process comes into the ULSI era, process control becomes more important. The purpose of fault detection of fabrication process is to increase the utility rate of equipment and to improve the product quality. In order to maintain competitive production capacity and product quality, process fault detection is a very important issue. This thesis studies the thin-film fabrication in the array process of TFT-LCD. Data obtained from the RF supply system of PECVD is studied. This proposed fault detection tool will capture the characteristics of RF signals using Kohonen competitive network and Fuzzy C Means. Then, the secure and warning areas of training model are defined by ellipsoidal calculus. This constructed model can be utilized to detect any malfunctions and faults occurred during the semiconductor manufacturing. In our experiment, 22 sets of normal data were used as the training data for Kohonen network and, furthermore, these obtained feature neurons were classified into two groups by Fuzzy C Means in order to filter out the scattered neurons. Then, the secure and the warning areas of training model were calculated by the ellipsoidal calculus. Next, 10 sets of normal data were used to test the generalization of the training model. If any erroneous judgments occur, the model must be re-trained by adding these data for improving the generalization. Finally, four different kinds of faults, including chamber, solenoid valve, matching box and generator, were examined using 9 sets of data per faulty type. The feasibility of fault detection tool was verified.