Adaptive Neuro-Fuzzy Approach for Predicting Hardness of Deposited TiN/ZrN Multilayer Coatings
This paper presents an adaptive neuro-fuzzy approach based on first order function of fuzzy model for establishing the relationship between control factors and thin films properties of TiN/ZrN coatings on Si(100) wafer substrates. A statistical model was designed to explore the space
of the processes by an orthogonal array scheme. Eight control factors of closed unbalance magnetron sputtering system were selected for modeling the process, such as interlayer material, argon and nitrogen flow rate, titanium and zirconium target current, rotation speed, work distance, and
bias voltage. Analysis of variance (ANOVA) was carried out for determining the influence of control factors. In this study, with the application of ANOVA, the smallest effect of control factors was eliminated. The adaptive neuro-fuzzy inference system (ANFIS) was applied as a tool to model
the deposited process with five significant control factors. The experimental results show that ANFIS demonstrates better accuracy than additive model for the film hardness. The root mean square error between prediction values and experimental values were archived to 0.04.
Keywords: ANALYSIS OF VARIANCE; ANFIS; MULTILAYERS; TIN/ZRN
Document Type: Research Article
Publication date: 01 July 2010
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