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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
scientific.net
2013
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf http://umpir.ump.edu.my/id/eprint/4705/ http://dx.doi.org/10.4028/www.scientific.net/NH.4.21 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831521623930306560 |
|---|---|
| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.4705 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2013 |
| publisher | scientific.net |
| record_format | eprints |
| spelling | my.ump.umpir.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. (Published) http://dx.doi.org/10.4028/www.scientific.net/NH.4.21 DOI: 10.4028/www.scientific.net/NH.4.21 |
| 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 |
| title | 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_short | 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 |
| topic | Q Science (General) |
| url | http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf http://umpir.ump.edu.my/id/eprint/4705/ http://dx.doi.org/10.4028/www.scientific.net/NH.4.21 |
| url_provider | http://umpir.ump.edu.my/ |