DocumentCode :
2636528
Title :
Intelligent Systems and Applications The Predictions of Luminous Intensity and Wavelength of Light-Emitting Diode by Neural Network
Author :
Weng, Pin-Hsuan ; Liu, Fang-Tsung ; Huang, Huang-Chu ; Chen, Yu-Ju ; Hwang, Rey-Chue
Author_Institution :
Dept. of Electr. Eng., I-Shou Univ., Kaohsiung
fYear :
2008
fDate :
18-20 June 2008
Firstpage :
293
Lastpage :
293
Abstract :
The aim of this research is to predict the luminous intensity and wavelength of light-emitting diode (LED) chip by using neural network technique. The data simulated was measured by electrical luminescence (EL) technique. The well trained neural model could be used to predict the optoelectronic attributes of LED chip in advance. The predicted results are expected to help the engineer can modify the parameters of epitaxy growth accurately to ensure the chip can be in conformance with the quality request.
Keywords :
electroluminescent devices; electronic engineering computing; epitaxial growth; light emitting diodes; neural nets; LED chip; electrical luminescence technique; epitaxy growth; intelligent systems; light-emitting diode wavelength; luminous intensity prediction; neural network; optoelectronic attributes; Electric variables measurement; Intelligent networks; Intelligent systems; Light emitting diodes; Luminescence; Neural networks; Predictive models; Semiconductor device measurement; Semiconductor process modeling; Wavelength measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
Type :
conf
DOI :
10.1109/ICICIC.2008.327
Filename :
4603482
Link To Document :
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