A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example

Abstract

To increase the ecological sustainability of manufacturing, enhancing the yield of each product is a critical task that eliminates waste and increases profitability. An equally crucial task is to estimate the future yield of each product so that the majority of factory capacity can be allocated to products that are expected to have higher yields. To this end, a fuzzy collaborative intelligence (FCI) approach is proposed in this study. In this FCI approach, a group of domain experts is formed. Each expert constructs an artificial neural work (ANN) to fit an uncertain yield learning process for estimating the future yield with a fuzzy value; in past studies, however, uncertain yield learning processes were modeled only by solving mathematical programming problems. In this research, fuzzy yield estimates from different experts were aggregated using fuzzy intersection. Then, the aggregated result was defuzzified with another ANN. A real dynamic random access memory case was utilized to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology outperformed five existing methods in improving the estimation accuracy, which was measured in terms of the mean absolute error and the mean absolute percentage error.