A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

[1]  Wojciech Samek,et al.  Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..

[2]  Tharam S. Dillon,et al.  Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming , 2011, Appl. Soft Comput..

[3]  Adil Baykasoglu,et al.  New approaches to due date assignment in job shops , 2008, Eur. J. Oper. Res..

[4]  Toly Chen,et al.  Incorporating the FCM-BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant , 2010 .

[5]  Ali Azadeh,et al.  A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns , 2011, Concurr. Eng. Res. Appl..

[6]  V. Vinod,et al.  Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system , 2011 .

[7]  Wenjun Chris Zhang,et al.  Big data driven cycle time parallel prediction for production planning in wafer manufacturing , 2018, Enterp. Inf. Syst..

[8]  Nur Evin Özdemirel,et al.  Manufacturing lead time estimation using data mining , 2006, Eur. J. Oper. Res..

[9]  Toly Chen,et al.  An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication , 2015, Int. J. Intell. Syst..

[10]  Dvir Shabtay,et al.  Scheduling and due date assignment to minimize earliness, tardiness, holding, due date assignment and batch delivery costs , 2010 .

[11]  Israel Tirkel.  The effectiveness of variability reduction in decreasing wafer fabrication cycle time , 2013, 2013 Winter Simulations Conference (WSC).

[12]  Adan Valles,et al.  Implementation of Six Sigma in a Manufacturing Process: A Case Study , 2009 .

[13]  A. Nagoor Gani,et al.  A New Operation on Triangular Fuzzy Number for Solving Fuzzy Linear Programming Problem , 2012 .

[14]  Junliang Wang,et al.  A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System , 2018, IEEE Transactions on Semiconductor Manufacturing.

[15]  Ilgin Gokasar,et al.  Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management , 2021 .

[16]  Ping-Huan Kuo,et al.  A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting , 2018 .

[17]  Pei-Chann Chang,et al.  A case-based expert support system for due-date assignment in a wafer fabrication factory , 2003, J. Intell. Manuf..

[18]  Toly Chen,et al.  Asymmetric cycle time bounding in semiconductor manufacturing: an efficient and effective back-propagation-network-based method , 2016, Oper. Res..

[19]  Michael Hanss,et al.  Applied Fuzzy Arithmetic , 2005 .

[20]  Xu Tan,et al.  Analysis of production cycle-time distribution with a big-data approach , 2020, J. Intell. Manuf..

[21]  Toly Chen,et al.  A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory , 2017, J. Intell. Manuf..

[22]  Pei-Chann Chang,et al.  Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory , 2006, Appl. Soft Comput..

[23]  Wei He,et al.  Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  D. Pamučar,et al.  Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology. , 2021, The Science of the total environment.

[25]  Toly Chen,et al.  A collaborative fuzzy-neural approach for internal due date assignment in a wafer fabrication plant , 2011 .

[26]  Wen Yu,et al.  Fuzzy identification using fuzzy neural networks with stable learning algorithms , 2004 .

[27]  Dan Ding,et al.  A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems , 2016, Appl. Soft Comput..

[28]  Ali Azadeh,et al.  An intelligent algorithm for optimum forecasting of manufacturing lead times in fuzzy and crisp environments , 2013 .

[30]  Jie Zhang,et al.  Big data analytics for forecasting cycle time in semiconductor wafer fabrication system , 2016 .

[31]  Rudolf Kruse,et al.  A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation , 1993, IEEE International Conference on Neural Networks.

[32]  Israel Tirkel,et al.  Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases , 2013 .

[33]  Jie Zhang,et al.  Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system , 2020, Comput. Ind. Eng..

[34]  Chen-Fu Chien,et al.  Efficient development of cycle time response surfaces using progressive simulation metamodeling , 2014 .

[35]  Joseph P. Martino,et al.  A review of selected recent advances in technological forecasting , 2003 .

[36]  Yi-Feng Hung,et al.  DISPATCHING RULES USING FLOW TIME PREDICTIONS FOR SEMICONDUCTOR WAFER FABRICATIONS , 2002 .

[37]  Biswajit Sarkar,et al.  Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction , 2019, Mathematics.

[38]  Pei-Chann Chang,et al.  A fuzzy neural network for the flow time estimation in a semiconductor manufacturing factory , 2008 .

[39]  Junliang Wang,et al.  Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems , 2018, IEEE Transactions on Industrial Informatics.

[40]  H. Ishibuchi,et al.  A learning algorithm of fuzzy neural networks with triangular fuzzy weights , 1995 .

[41]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[42]  Jih-Chang Hsieh,et al.  A neural networks approach for due-date assignment in a wafer fabrication factory , 2003 .

[43]  T. C. Edwin Cheng,et al.  Common due date assignment and scheduling with a rate-modifying activity to minimize the due date, earliness, tardiness, holding, and batch delivery cost , 2012, Comput. Ind. Eng..