標題: Forecasting the yield of a semiconductor product using a hybrid-aggregation and entropy-consensus fuzzy collaborative intelligence approach
作者: Chen, Toly
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Fuzzy collaborative intelligence;Yield;Semiconductor;Fuzzy weighted average;Entropy
公開日期: 1-Aug-2019
摘要: 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 (FL). 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. (C) 2019 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.measurement.2019.04.070
http://hdl.handle.net/11536/151895
ISSN: 0263-2241
DOI: 10.1016/j.measurement.2019.04.070
期刊: MEASUREMENT
Volume: 142
起始頁: 60
結束頁: 67
Appears in Collections:Articles