Fault Detection and Classification of PECVD Equipment Using Neural Network

• Main Authors: David Chiu, 邱瑞隆
• Other Authors: none
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/84962085151394276005

碩士 === 中原大學 === 機械工程研究所 === 97 === Currently, the thin film processes are the major process in the TFT industry. Among all the thin film processes, the chemical vapor deposition process, or CVD, is especially important for processing nonmetallic thin films. Although, over the years, different CVDs are developed and adopted in the manufacturing processes, a common and more advantageous CVD has emerged for the TFT panel display production process. As the large-size panel display becomes the mainstream product, manufacturers need to be particularly benefit and cost-conscious about the TFT-LCD manufacturing processes and equipments. Therefore, lowering the equipment faculty ratio and improving the product non-defect ratio become the urgent goals for all TFT-LCD manufacturers. This study adopts the Artificial Neural Network theory, coupled with the Euclid principle, to detect possible faults for the PECVD equipments and their plasma RF supply subsystems. These commonly reported faults are then categorized. Although the PECVD equipments have a built-in fault detection mechanism for detecting the RF supply subsystem, the fault isolation process for root cause is still largely carried out by skilled on-site technicians. To improve the fault isolation process, this study adopts the regional analysis approach to correctly point out the equipment faulty location, reduce the equipment down time, and improve equipment utilization rate. The proposed regional analysis approach first uses an Neural Network model to gather normal operational data and abnormal data of a PECVD equipment, and then compares and contrasts both data sets to generate practical faulty waveforms. This is achieved by using SOFM and Euclid principles to clarify the faulty factors and isolate fuzzy regions, which is a distinguished advantage of the regional analysis approach. This approach can complement the equipment delectability and strengthen the warning function for a PECVD equipment. With this approach, the equipment down time can be significantly shortened and equipment utilization rate improved, and most importantly, the product defect rate, caused by inaccurate judgments for problem identification in the production process, can be drastically lowered.