Using expert technology to select unstable slicing machine to control wafer slicing quality via fuzzy AHP

Abstract

Silicon wafer slicing is an increasingly complex manufacturing process. This involves high purity levels, crystallographic perfection and precise mechanical tolerances, thus 12 in. 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 why yields decline. The criteria for establishing the proposed algorithm are derived from literature review and modified Delphi method in semiconductor manufacturing. The main objective of this paper is to propose a new approach within the AHP framework for tackling the uncertainty and imprecision of silicon wafer slicing evaluations during manufacturing process stages, where the decision-maker’s comparison judgments are represented as fuzzy triangular numbers. Additionally, the proposed algorithm can select the evaluation outcomes to identify the worst machine of precision. Finally, results of EWMA control chart demonstrate the feasibility of the proposed fuzzy 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 fuzzy AHP, it will the key to help the engineer can find out the manufacturing process yield quickly effectively.