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

fYear

2006

fDate

25-28 June 2006

Firstpage

1866

Lastpage

1871

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICMA.2006.257519

Filename

4026378