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Vol. 133, Issue 10, October 2011, pp.8-17




Neural Net Based Optimization of Wet Thermal Lateral Oxidation Rates


1 Mohd Sami Ashhab, 2 Nabeel Abo Shaban and  3 Abdulla N. Olimat

1 Mechanical Engineering Department, The Hashemite University, Zarqa 13115, Jordan

Tel.: +962-5-390-3333, fax: +962-5-382-6348

2 Mechanical Engineering Department, The University of Jordan, Amman 11942, Jordan

Tel.: +962-6-535-5000, fax: +962-6-535-5588

 3 Mechanical Engineering Department, The University of Jordan, Amman 11942, Jordan

Tel.: +962-6-535-5000, fax: +962-6-535-5588

E-mail: sami@hu.edu.jo, aboshaban65@yahoo.com, olimat2008@yahoo.com



Received: 29 September 2011   /Accepted: 25 October 2011   /Published: 31 October 2011

Handbook of Laboaratory Measurements book


Abstract: Critical parameters, AlAs mole fraction, temperature of the sample and the carrier gas flow must be controlled to establish a repeatable and uniform oxidation process. Modeling and simulation of these parameters has enabled the compilation of oxidation rate data for AlGaAs which exhibits Arrhenius rate dependence. The output is related to the inputs of the process by an artificial neural net model which is trained with historical input-output data. The data is originally extracted and manipulated from experimental laboratories measurements. The proposed method is tested through computer simulation and the results demonstrate the effectiveness of the code and the algorithm. The objective of this study is the prediction of lateral oxidation rates at variances of temperature and mole fraction for different compositions. This is done through optimization techniques.


Keywords: Experimental measurements, Neural networks, Optimization, Modeling, MEMS lateral oxidation


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