Papers by Author: Tian Ming Li

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Authors: Wen Liang Tang, Chun Yue Huang, Tian Ming Li, Ying Liang, Guo Ji Xiong, Song Wu, Chun Quan Li, Zhong Ping Ning
Abstract: In this paper, ANSYS-LSDYNA simulation software is used to build the three-dimensional finite element model of the ball bond and to get the Von Mises stress. The change of stress about the bump is researched which base on the model in different bonding pressure, bonding power and bonding time. The result show that: The stress increase with bonding pressure increase within a certain bonding pressure range, and then the stress will maintain a table number, however, the stress will continue to increase when the bonding pressure reach a certain value; increasing the bonding power, the area of lager stress will grow; prolonging the bonding time, the stress of the pad will increase with time, but when time increase to a certain value, the stress of the pad will not increase over time.
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Authors: Chun Yue Huang, He Geng Wei, Tian Ming Li, De Jin Yan
Abstract: By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.
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Authors: Chun Yue Huang, Tian Ming Li, Ying Liang, He Geng Wei
Abstract: In the thermal design of embedded multi-chip module (MCM), the placement of chips has a significant effect on temperature field distributing, thus influences the reliability of the embedded MCM. The thermal placement optimization of chips in embedded MCM was studied in this paper, the goal of this work is to decrease temperature and achieve uniform thermal field distribution within embedded MCM. By using ANSYS the finite element analysis model of embedded MCM was developed, the temperature field distributing was calculated. Based on genetic algorithms, chips placement optimization algorithm of embedded MCM was proposed and the optimization chips placement of embedded MCM was achieved by corresponding optimization program. To demonstrate the effectiveness of the obtained optimization chips placement, finite element analysis (FEA) was carried out to assess the thermal field distribution of the optimization chips placement in embedded MCM by using ANSYS. The result shows that without chips placement optimizing the maximum temperature and temperature difference in embedded MCM model are 87.963°C and 2.189°C respectively, by using chips placement optimization algorithm the maximum temperature drop than the original 0.583°C and the temperature difference is only 0.694°C . It turns out that the chip placement optimization approach proposed in this work can be effective in providing thermal optimal design of chip placement in embedded MCM.
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