Analytic network process decision-making to assess slicing machine in terms of precision and control wafer quality

We discuss and develop a manufacturing quality yield model to forecast the 12 in silicon wafer slicing based on an analytic network process (ANP) framework. The ANP is a general theory of relative measurement used to derive composite-priority-ratio scales from individual-ratio scales that represent the relative influence of factors that interact with respect to the control criteria. Through its supermatrix, which is composed of matrices of column priorities, the ANP framework captures the outcome of dependence and feedback within and between clusters of factors. Additionally, the proposed algorithm can select the evaluation outcomes to identify the optimal machine of precision. Finally, results of the EWMA control chart and Process Capability Indices demonstrate the feasibility of the proposed ANP-based algorithm in effectively selecting the evaluation outcomes and in evaluating the precision of the optimal performing machines. We illustrate how the ANP model implemented for helping the engineer can find out the manufacturing process yield quickly and effectively.