bullet Sensors & Transducers Journal

    (ISSN 1726- 5479)

205.767

2008 e-Impact Factor

25 Top Downloaded Articles

Journal Subscription 2012

Editorial Calendar 2012

Submit an Article

Editorial Board

Current Issue

Sensors & Transducers journal's cover

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents

 

Best Articles 2010

 

 

 

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

 

Bullet

 

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

 

Acrobat reader logo Click <here> or title of paper to download the full pages article (1.34 Mb)

 

 

Read more about Chemical Sensors

 

 

 

 

 


1999 - 2011 Copyright , International Frequency Sensor Association (IFSA). All Rights Reserved.


Home - News - Links - Archives - Tools - Standardization - Patents - Marketplace - Projects - Wish List - Subscribe - Membership - IFSA Publishing - Submit Press Release - Twitter - Search

 Members Area -Sensors Portal - Voltage-to-Frequency Converters - Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Sensor Jobs - e-Shop - Site Map - Video