A study of screen/stencil printing on LTCC substrate

Author(s):  
Fei Zhao ◽  
Yuan-Lan Dang
2008 ◽  
Vol 23 (2) ◽  
pp. 182-187 ◽  
Author(s):  
Z. W. Zhong ◽  
P. Arulvanan ◽  
C. K. Goh

2016 ◽  
Vol 55 (7S1) ◽  
pp. 07KE15
Author(s):  
Tsukasa Kaneko ◽  
Taiga Kibe ◽  
Keisuke Kimoto ◽  
Ryota Nishimura ◽  
Makiko Kobayashi

Author(s):  
Goran J. Radosavljević ◽  
Andrea M. Marić ◽  
Walter Smetana ◽  
Ljiljana D. Živanov
Keyword(s):  
Air Gap ◽  
Q Factor ◽  
High Q ◽  

1999 ◽  
Author(s):  
Jianbiao Pan ◽  
Gregory L. Tonkay

Abstract Stencil printing has been the dominant method of solder deposition in surface mount assembly. With the development of advanced packaging technologies such as ball grid array (BGA) and flip chip on board (FCOB), stencil printing will continue to play an important role. However, the stencil printing process is not completely understood because 52–71 percent of fine and ultra-fine pitch surface mount assembly defects are printing process related (Clouthier, 1999). This paper proposes an analytical model of the solder paste deposition process during stencil printing. The model derives the relationship between the transfer ratio and the area ratio. The area ratio is recommended as a main indicator for determining the maximum stencil thickness. This model explains two experimental phenomena. One is that increasing stencil thickness does not necessarily lead to thicker deposits. The other is that perpendicular apertures print thicker than parallel apertures.


2010 ◽  
Vol 156-157 ◽  
pp. 10-17 ◽  
Author(s):  
Er Shun Pan ◽  
Yao Jin ◽  
Zhao Mei ◽  
Ying Wang

A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval , response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.


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