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

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

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 di...

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Main Authors: M. M., Yusoff, Mehdi, Qasim, Al-Dabbagh, Jinan B., Abdalla, Ahmed N., Hegde, Gurumurthy
Format: Article
Language:English
Published: scientific.net 2013
Subjects:
Q Science (General)
Online Access:http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf
id ump-4705
recordtype eprints
spelling ump-47052018-10-03T07:38:10Z http://umpir.ump.edu.my/id/eprint/4705/ Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition M. M., Yusoff Mehdi, Qasim Al-Dabbagh, Jinan B. Abdalla, Ahmed N. Hegde, Gurumurthy Q Science (General) 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. scientific.net 2013-05 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf 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 http://dx.doi.org/10.4028/www.scientific.net/NH.4.21 DOI: 10.4028/www.scientific.net/NH.4.21
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic Q Science (General)
spellingShingle Q Science (General)
M. M., Yusoff
Mehdi, Qasim
Al-Dabbagh, Jinan B.
Abdalla, Ahmed N.
Hegde, Gurumurthy
Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
description 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.
format Article
author M. M., Yusoff
Mehdi, Qasim
Al-Dabbagh, Jinan B.
Abdalla, Ahmed N.
Hegde, Gurumurthy
author_facet M. M., Yusoff
Mehdi, Qasim
Al-Dabbagh, Jinan B.
Abdalla, Ahmed N.
Hegde, Gurumurthy
author_sort M. M., Yusoff
title Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
title_short Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
title_full Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
title_fullStr Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
title_full_unstemmed Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition
title_sort radial basis function neural network model for optimizing thermal annealing process operating condition
publisher scientific.net
publishDate 2013
url http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf
first_indexed 2023-09-18T21:59:33Z
last_indexed 2023-09-18T21:59:33Z
_version_ 1777414306712780800

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