Title :
Prediction of Laser Cutting Quality for QFN Package by using Neural Network
Author :
Tsai, Ming-Jong ; Li, Chen-Hao ; Chen, Cheng-Che ; Yao, Sin-Min
Author_Institution :
Graduate Inst. of Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei
Abstract :
This paper reports the predictions of laser cutting QFN (quad flat non-lead) packages by using Levenberg-Marquardt backpropagation algorithm of neural network. A mathematical model via neural network was proposed for predicting the laser six cutting quality. A 5times5 QFN package was cutting by using a diode pumped solid state laser system (DPSSL) in this paper. From the predicted results, six average errors of cutting qualities are 0.6%, 1.24%, 3.21%, 2.44%, 5.08% and 13.73%. The results may give guides in the predictions of cutting QFN packages and is expected to be useful for laser applications in other industry fields
Keywords :
backpropagation; laser beam cutting; neural nets; production engineering computing; semiconductor device packaging; Levenberg-Marquardt backpropagation algorithm; diode pumped solid state laser system; laser cutting quality prediction; neural network; quad flat nonlead packages; Backpropagation algorithms; Diodes; Laser beam cutting; Laser excitation; Laser modes; Mathematical model; Neural networks; Packaging; Pump lasers; Solid lasers; Laser cutting; Neural networrk; QFN package;
Conference_Titel :
Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on
Conference_Location :
Luoyang, Henan
Print_ISBN :
1-4244-0465-7
Electronic_ISBN :
1-4244-0466-5
DOI :
10.1109/ICMA.2006.257519
