Optimal Improvement on Cutting Yield Rate in ACF Attach Process of TFT-LCD Module Using Response Surface Method

2010 ◽  
Vol 126-128 ◽  
pp. 208-213
Author(s):  
Jian Long Kuo ◽  
Chung Hao Hsieh

In TFT-LCD manufacturing process, the ACF is an essential material. To make the driving circuit conductive, the ACF attach process is used in bonding process. Since the total manufacturing cost becomes lower year by year, the ACF material occupies a great deal of manufacturing cost. The boding technology has been changed from the conventional long bar type into short bar type to save the material usage. The parameter setting of the short bar type machine was not initially optimized. The NG rate of short bar type ACF attach process is higher as compared to the long bar type. The rework cost and material cost may increase in the short bar type process. Therefore, the parameter optimization for the associated short bar type ACF attach process becomes an essential problem. The response surface method is adopted to model the problem. The yield rate is selected as objective function for study. In the analysis of response surface method, the plasma clean speed, ACF peeling speed and ACF cutter spring setting are selected three key factors for discussion. Results reveal that the yield rate can be improved up to 99.35%, which is very helpful to improve the manufacturing process.

2005 ◽  
Vol 38 (1) ◽  
pp. 253-258
Author(s):  
Daniel LEPADATU ◽  
Xavier BAGUENARD ◽  
Abdessamad KOBI ◽  
Ridha HAMBLI ◽  
Luc JAULIN

2016 ◽  
Vol 30 (1) ◽  
pp. 345-352 ◽  
Author(s):  
Abdulrahman Al-Ahmari ◽  
Mohammad Ashfaq ◽  
Abdullah Alfaify ◽  
Basem Abdo ◽  
Abdulrahman Alomar ◽  
...  

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.


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