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.
|Number of pages||8|
|Journal||Measurement: Journal of the International Measurement Confederation|
|State||Published - 1 Aug 2019|
- Fuzzy collaborative intelligence
- Fuzzy weighted average