Forecasting the yield of a semiconductor product using a hybrid-aggregation and entropy-consensus fuzzy collaborative intelligence approach

Abstract

Forecasting the yield of each product is critical for a semiconductor manufacturer. To enhance the performance of forecasting the yield of a semiconductor product, a hybrid-aggregation and entropy-consensus fuzzy collaborative intelligence (FCI) approach is proposed in this study. The novelty of the proposed approach is in its use of a hybrid aggregation mechanism that first aggregates fuzzy yield forecasts by using a fuzzy weighted average (FWA) and then adjusts the FWA result by using fuzzy intersection (FI). In this way, both subjective and objective viewpoints are considered in forecasting the yield of a semiconductor product. In addition, the consensus among experts is measured with the entropy of the aggregation result. After consensus is reached, the aggregation result is defuzzified using a back propagation network (BPN). The effectiveness of the proposed methodology is validated by analyzing a real case. According to the analysis results, the forecasting accuracy, measured in terms of mean absolute error (MAE) or mean absolute percentage error (MAPE), improved considerably when using the proposed methodology.