Using Genetic Algorithm to Establish Thresholds of Wafer Retesting When Maximizing Profit

• Main Authors: Pei-ru Weng, 翁佩如
• Other Authors: Gwo-gi Sheen
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/71722295105799960644

碩士 === 國立中央大學 === 工業管理研究所 === 96 === In order to verify whether wafer can be able to achieve expected specification, generally every die will be probed completely after being fabricated in wafer fab. By such operation, we can use the essential index to improve wafer foundry’s quality and also the yield rate of wafers. In the mean time, IC design house hopes to get better yield rate during wafer probing process, normally they will try to retest low-yield-wafer, no matter retest entire gross dies or particular defective dies. In general, defective dies are classified by using different bin numbers; the bin numbers represent particular testing result or its performance. During failure analysis after finishing wafer testing, testing engineer can decide to re-test abnormal wafer directly if it’s out of yield limit set previously. As normally engineers just ‘hope’ to get higher yield recovered from second testing, they seldom know how to predict yield variation and regard the related profit before making decision of re-testing Hence, this thesis attempts to propose a workable solution for wafer testing process by using Genetic Algorithm to establish thresholds of wafer retesting. Through a real example from CMOS Image Sensor probing process, it was presented to demonstrate the methodology. The result of experiment show that the profit of this study is 13182474, and it is greater than the profit of case company, increases 38186. If we only discuss the retesting wafer, the profit is 2711771.2, compares with case company, and its improvement is 114.62%. So that could say Genetic Algorithm has a not bad solution with threshold of wafer retesting in this thesis.