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Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition

M. M., Yusoff and Mehdi, Qasim and Al-Dabbagh, Jinan B. and Abdalla, Ahmed N. and Hegde, Gurumurthy (2013) Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition. Nano Hybrids , 4. pp. 21-31. ISSN 2234-9871

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Abstract

Optimum thermal annealing process operating condition for nanostructured porous silicon (nPSi) by using radial basis function neural network (RBFNN) was proposed. The nanostructured porous silicon (nPSi) layer samples prepared by electrochemical etching process (EC) of p-type silicon wafers under different operatingconditions, such as varyingetchingtime (Et), annealing temperature (AT), and annealing time (At). The electrical properties of nPSi show an enhancement with thermal treatment.Simulation result shows that the proposed model can be used in the experimental results in this operating condition with acceptable small error. This model can be used in nanotechnology based photonic devices and gas sensors.

Item Type: Article
Additional Information: Profesor Madya Dr. Ahmed N Abd Alla (A. N. Abdalla)
Uncontrolled Keywords: Electrochemical Etching, Nanostructured Porous Silicon, Radial Basis Function, Thermal Annealing
Subjects: Q Science > Q Science (General)
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Prof. Dr. Mashitah Mohd Yusoff
Date Deposited: 31 Dec 2013 07:56
Last Modified: 03 Oct 2018 07:38
URI: http://umpir.ump.edu.my/id/eprint/4705
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