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.
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References
Ahmadi A, Stratigopoulos HG, Nahar A, Orr B, Pas M, Makris Y (2015) Yield forecasting in fab-to-fab production migration based on Bayesian model fusion. In: Proceedings of the IEEE/ACM international conference on computer-aided design. pp 9–14
Bonnans JF, Gilbert JC, Lemaréchal C, Sagastizábal CA (2006) Numerical optimization: theoretical and practical aspects. Springer, Berlin
Chen T (2009) Estimating and incorporating the effects of a future QE project into the semiconductor yield learning model with a fuzzy set approach. Eur J Ind Eng 3(2):207–226
Chen T, Chiu M-C (2015) An improved fuzzy collaborative system for predicting the unit cost of a DRAM product. Int J Intell Syst 30:707–730
Chen T, Lin Y-C (2008) A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. Int J Uncertain Fuzziness Knowl Based Syst 16(1):35–58
Chen T, Wang MJJ (1999) A fuzzy set approach for yield learning modeling in wafer manufacturing. IEEE Trans Semicond Manuf 12(2):252–258
Chen T, Wang Y-C (2013) Semiconductor yield forecasting using quadratic-programming based fuzzy collaborative intelligence approach. Math Probl Eng 2013:Art Id 672404
Chen T, Wang YC (2014) An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting. IEEE Trans Fuzzy Syst 22(1):201–211
Donoso S, Marin N, Vila MA (2006) Quadratic programming models for fuzzy regression. In: Proceedings of international conference on mathematical and statistical modeling in honor of Enrique Castillo
Feng J, Kang C, Xubing Y (2016) Risk evaluation method of ocean platform based on triangular fuzzy number and AHP. J Gansu Sci 3:014
Gruber H (1992) The yield factor and the learning curve in semiconductor production. Appl Econ 24(8):885–894
Gruber H (1994) Learning and strategic product innovation: theory and evidence for the semiconductor industry. Elsevier, Amsterdam
Huerta I, Biasi P, García-Serna J, Cocero MJ, Mikkola J-P, Salmi T (2016) Continuous H2O2 direct synthesis process: an analysis of the process conditions that make the difference. Green Process Synth 5(4):341–351
Lin JS (2012) Constructing a yield model for integrated circuits based on a novel fuzzy variable of clustered defect. Expert Syst Appl 39(3):2856–2864
Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 121:1585–1588
Moyne J, Ward N, Stafford R (2014) Yield prediction feedback for controlling an equipment engineering system. U.S. Patent No. 8774956
Mullenix P, Zalnoski J, Kasten AJ (1997) Limited yield estimation for visual defect sources. IEEE Trans Semicond Manuf 10:17–23
Parreiras RO, Ekel PY, Morais DC (2012) Fuzzy set based consensus schemes for multicriteria group decision making applied to strategic planning. Group Decis Negot 21(2):153–183
Peters G (1994) Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst 63(45–55):1994
Roh SB, Ahn TC, Pedrycz W (2012) Fuzzy linear regression based on polynomial neural networks. Expert Syst Appl 39(10):8909–8928
Rusinko C (2007) Green manufacturing: an evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes. IEEE Trans Eng Manag 54(3):445–454
Silvert W (2000) Fuzzy indices of environmental conditions. Ecol Model 130(1):111–119
Tanaka H, Watada J (1988) Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst 272:275–289
Tirkel I (2013) Yield learning curve models in semiconductor manufacturing. IEEE Trans Semicond Manuf 26(4):564–571
Wang J, Ding D, Liu O, Li M (2016) A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems. Appl Soft Comput 39:11–20
Weber C (2004) Yield learning and the sources of profitability in semiconductor manufacturing and process development. IEEE Trans Semicond Manuf 17(4):590–596
Xiao C, Xue Y, Li Z, Luo X, Qin Z (2015) Measuring user influence based on multiple metrics on YouTube. In: The seventh international symposium on parallel architectures, algorithms and programming. pp 177–182
Zhang X, Ouyang K, Liang J, Chen K, Tang X, Han X (2016) Optimization of process variables in the synthesis of butyl butyrate using amino acid-functionalized heteropolyacids as catalysts. Green Process Synth 5(3):321–329
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This study was sponsored by the Ministry of Science and Technology, Taiwan.
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Chen, T., Wang, YC. A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example. Oper Res Int J 18, 671–688 (2018). https://doi.org/10.1007/s12351-017-0312-y
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DOI: https://doi.org/10.1007/s12351-017-0312-y