The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
Transactions ID Number: 52-240
Full Name: Guido Guizzi
Position: Researcher
Age: ON
Sex: Male
Address: P.le Tecchio 80, Naples
Country: ITALY
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E-mail address: g.guizzi@unina.it
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Title of the Paper: Robust Design for Etching Process Parameters of Hard Disk Drive Slider Fabrication Using Data Mining and Multi Response Optimization
Authors as they appear in the Paper: Pongsak Holimchayachotikul, Komgrit Leksakul, Guido Guizzi
Email addresses of all the authors: holimchayachotikul@hotmail.com, komgrit@eng.cmu.ac.th, g.guizzi@unina.it
Number of paper pages: 10
Abstract: This paper is to provide a good insight into solving a multi-response optimization problem using a hybrid response surface methodology (RSM) based on robust parameter design (RPD) concept and data mining (DM) for the multi-response optimization of a RIE process. Over the recent years in many high precision manufacturing organizations, the domain experience and engineering judgment have been handled the multiple response optimization problems. These manners lead to increase in uncertainty during the decision-making process. This situation has also happened in the hard disk drive fabrication based on reactive ion etching (RIE). This process is much more complicated to set its parameters to the slider with the right customer specification. Therefore, this paper presents a hybrid model to optimize the concerning responses of this process in tern of mean and variance. The silicon plates with a patterned wet film photo resistance as a base substrate are used to demonstra!
te this research. To begin with the proposed approach, Design of experiment, named central composite design (CCD), was employed to accumulate the process records and to specify the significant parameters of the process. Then, support vector regression (SVR) was brought into play to institute the nonlinear multivariate relationships between process parameters and responses. Data obtained from DOE were used in the training process. Last but not least, the regression decision tree and the domain engineering knowledge were opted for the initial point of optimization algorithm as well. In conclusion, the reduced gradient search algorithm, a hill-climbing procedure and desirability function were adapted to the DOE Model. While grid search and desirability function were adapted to the SVR model to find the optimum parameter setting. SVR is the technique with the highest prominent accuracy performance, so it was selected to construct a RIE process model. Consequently, the optimum!
condition from the final model is efficiently enabled to apply in the
real production based on its confirmation experiment.
Keywords: Reactive Ion Etching (RIE), Response Surface Methodology (RSM), Support Vector Regression (SVR), Multi-response Optimization, Robust Parameter Design (RPD), Data Mining (DM)
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