Prediction of etching rate of alumino-silicate glass by RSM and ANN

被引：0

作者：

Ting, H. T. ^{[1
]}

Abou-El-Hossein, K. A. ^{[2
]}

Chua, H. B. ^{[1
]}

机构：

[1] Curtin Univ Technol, Dept Mech Engn, Miri, Sarawak 68006, Australia

[2] Nelson Mandela Metropolitan Univ, Dept Mechatron, ZA-6031 Port Elizabeth, South Africa

来源：

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH | 2009年 / 68卷 / 11期

关键词：

Alumino-silicate glass; ANN; Etching rate; RSM; SURFACE METHODOLOGY RSM; NEURAL-NETWORK; OPTIMIZATION; WAFER;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, response surface methodology (RSM) and artificial neural network (ANN) were applied to predict material removal rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2(k) Factorial design was performed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and quadratic model was found most significant for data values of process parameters. New models were able to predict etching rate of alumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setup with and without-condensation.

引用

页码：920 / 924

页数：5

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