Fuzzy TOPSIS for Multiresponse Quality Problems in Wafer Fabrication Processes

The quality characteristics in the wafer fabrication process are diverse, variable, and fuzzy in nature. How to effectively deal with multiresponse quality problems in the wafer fabrication process is a challenging task. In this study, the fuzzy technique for order preference by similarity to an ideal solution (TOPSIS), one of the fuzzy multiattribute decision-analysis (MADA) methods, is proposed to investigate the fuzzy multiresponse quality problem in integrated-circuit (IC) wafer fabrication process. The fuzzy TOPSIS is one of the effective fuzzy MADA methods for dealing with decision-making problems under uncertain environments. First, a fuzzy TOPSIS methodology is developed by considering the ambiguity between quality characteristics. Then, a detailed procedure for the developed fuzzy TOPSIS approach is presented to show how the fuzzy wafer fabrication quality problems can be solved. Real-world data is collected from an IC semiconductor company and the developed fuzzy TOPSIS approach is applied to find an optimal combination of parameters. Results of this study show that the developed approach provides a satisfactory solution to the wafer fabrication multiresponse problem. This developed approach can be also applied to other industries for investigating multiple quality characteristics problems.

[1]  C. Fung,et al.  Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis , 2005 .

[2]  Lee-Ing Tong,et al.  Optimization of a multi-response problem in Taguchi's dynamic system , 2005, Comput. Ind. Eng..

[3]  Fabio Crestani,et al.  Soft computing in information retrieval: techniques and applications , 2000 .

[4]  P.B.S. Reddy,et al.  Unification of robust design and goal programming for multiresponse optimization : A case study , 1997 .

[5]  Lee-Ing Tong,et al.  Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks , 2001, Comput. Ind..

[6]  Milan Zelany,et al.  A concept of compromise solutions and the method of the displaced ideal , 1974, Comput. Oper. Res..

[7]  W. Marsden I and J , 2012 .

[8]  John W. Fowler,et al.  Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling , 2003 .

[9]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[10]  Celik Parkan,et al.  Selection of a manufacturing process with multiple attributes: A case study , 1995 .

[11]  Y. S. Tarng,et al.  Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics , 2000 .

[12]  Rita Almeida Ribeiro Fuzzy multiple attribute decision making: A review and new preference elicitation techniques , 1996, Fuzzy Sets Syst..

[13]  W. Baran Development of design methods for permanent magnet circuits, demonstrated with a core magnet assembly , 1980 .

[14]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[15]  Onur Köksoy,et al.  A nonlinear programming solution to robust multi-response quality problem , 2008, Appl. Math. Comput..

[16]  Kevin Tucker,et al.  Response surface approximation of pareto optimal front in multi-objective optimization , 2004 .

[17]  Tien-Chin Wang,et al.  Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment , 2007, Expert Syst. Appl..

[18]  Chao-Ton Su,et al.  Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm , 2003, Comput. Ind..

[19]  A. Rizzi,et al.  A fuzzy TOPSIS methodology to support outsourcing of logistics services , 2006 .

[20]  T. S. Lia,et al.  Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm , 2003 .

[21]  Hung-Chang Liao,et al.  A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method , 2004, Comput. Ind. Eng..

[22]  T. Chu,et al.  A Fuzzy TOPSIS Method for Robot Selection , 2003 .

[23]  Yu-Min Chiang,et al.  The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing , 2009, Comput. Ind. Eng..

[24]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.

[25]  Tung-Hsu Hou,et al.  Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process , 2008, Expert Syst. Appl..

[26]  Jiju Antony,et al.  Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function , 2001 .