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: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
scientific.net
2013
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| 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 |
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| Summary: | 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. |
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