Mining Knowledge Support for the Maintenance of Semiconductor Equipments

• Main Authors: Shu-Chen Liao, 廖淑珍
• Other Authors: Duen-Ren Liu
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/54291648744182099209

碩士 === 國立交通大學 === 管理學院碩士在職專班資訊管理組 === 93 === Semiconductor equipment is important asset in the semiconductor industry. Semiconductor equipment with high precision added value requires a more complete level of maintenance and management to satisfy organizations’ manufacturing operation needs. The condition of Semiconductor equipment affects the productivity and products’ pass rate of the manufacturing production system. Therefore, sound semiconductor equipment maintenance and management are key factors affecting enterprises’ viability in the increasingly fierce competitive market environment. Nowadays, though aggressive competitions and human resource mining prevailing in the high-tech industry, the job-change rate in general is very high. Once an employee leaves a job position, his/her maintenance knowledge and relevant skills will also soon disappear in the organization, and the reuse effect of the knowledge and skills is usually unsound. Data Mining is a more common approach these days, which is often adopted for data analysis. This can be achieved by employing Association Rule mining to discover the correlation among some items in databases. This research applies the Association Rule mining techniques to discover the correlation between error codes and maintenance codes. By storing the correlation as a word document, this work can then develop a fault diagnosis system for semiconductor equipment maintenance. This system can provide relevant repair suggestions to engineers performing repair functions. The proposed system can reduce the engineer’s repair time on the faulty machine, to increase maintenance efficiency and machine utilization rate; the system can also store the relevant knowledge in the organization, thus minimize potential damages to the company caused by employees’ resignation