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Silicon wafer slicing manufacturing process exhibits several characteristics. They are: (1) the product type is small batch production, (2) saw cutting must be very precise, (3) the process run time is long, and (4) inspecting samples is difficult. Furthermore, the process involves several synchronously occurred multiple quality characteristics, such as thickness (THK), bow and warp, which must be closely monitored and controlled. Synchronously monitoring multiple quality characteristics is more expensive than monitoring a single quality characteristic in the manufacturing process. The sizes of inspected samples in the existing process are difficult to maintain the quality control chart. Grey situation decision method is used to screen the worst quality control chart. Grey situation decision method is used to screen the worst quality characteristic from the synchronously occurred multiple quality characteristics to monitor the process. Finally, a case study is presented to demonstrate the feasibility and effectiveness of proposed decision method. The exponential weighted moving average (EWMA) control chart is used to verify that the process quality is more reliable.

This study describes a novel algorithm for optimizing the quality yield of silicon wafer slicing. 12 inch wafer slicing is the most difficult in terms of semiconductor manufacturing yield. As silicon wafer slicing directly impacts production costs, semiconductor manufacturers are especially concerned with increasing and maintaining the yield, as well as identifying whey yeilds decline. The criteria for establishing the proposed algorithm are derived from a literature review and interviews with a group of experts in semiconductor manufacturing. The modified Delphi method is then adopted to analyze those results. The proposed algorithm also incorporates the analytic hierarchy process (AHP) to determine the weights of evaluation. Additionally, the proposed algorithm can select the evaluation outcomes to identify the worst machine of precision. Finally, results of the exponential weighted moving average (EWMA) control chart demonstrate the feasibility of the proposed AHP‐based algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the worst performing machines. So, through collect data (the quality and quantity) to judge the result by AHP, it is the key to help the engineer can find out the manufacturing process yield quickly effectively.