Construction of thin film transistor LCD panel display end One Drop Filling Process Forecasting Mode

• Main Authors: ZHENG, JIN-FU, 鄭進福
• Other Authors: HUNG, LING-CHIEN
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/ya9s5w

碩士 === 國立中正大學 === 資訊管理學系碩士在職專班 === 105 === In the past decades, Taiwan was proud of TFT-LCD optoelectronics industry. Nowadays, facing international competitors and industry characteristics influenced by the economy of rapid globalization wheeled, it encounter survival crisis. For the crucial moment, various domestic industries are taking efforts to develop new the application of new technology or process technology to deal with this strange and varied industry. However, while domestic and foreign manufacturers are trying to breakthroughs, should the self-quality feedback to be considered to enhance the yield of production? Production yield is major competitiveness to production business. It not only influences utilization rate of various cost producers, but also roles the key business success factor. Therefore, how to enhance the competitiveness of the industrial yield is the important issue that every producer needs to face. Like discussed above, TFT-LCD panel industry is facing critical challenges. In addition to the technical specifications of self-promotion, to improve the quality basic yield should start from the fundamental. The ODF process is the extreme end of the TFT-LCD industry Glass critical process, and the production yield of TFT-LCD production influence overall cost more than 50%. How to increase industrial competitiveness by improving the process yield, improve the production yield and increase production efficiency by optimizing the production process or machine parameters is the key to survive in this strict and difficult market. This study is designed based on the data mining technology of industry 4.0. Small and medium size TFT-LCD panel manufacturer as target, through the current process parameters settings for each process, select study variables by different respectively attributes, and find out the key elements that impact the yield. First of all, getting data from researched object, select one set of attributes for the study target by Expert rule of thumb. Next, select impacting research topic attribute variables by weka.attributeSelection.GainRatioAttributeEval module. Finally, compile yield influenced factors from references, and then construct these three dimension reduction methods based on the result. After all research variables are pre-processed, take three sets of study variables, analyze and explore the impact of property hidden knowledge through data mining techniques four categories. Keywords: TFT-LCD panel ODF process, Industry 4.0, dimension reduction, data mining.