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Takagi-Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging

机译:Takagi-Sugeno神经模糊建模方法用于电子包装中的流体分配

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In the semiconductor manufacturing industry fluid dispensing is a popular process which is commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the fluid dispensing process is important because it enables us to understand the process behavior, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. Previous studies of fluid dispensing mainly focus on the development of analytical models. However, an analytical model for fluid dispensing, which can provide accurate results, is very difficult to develop because of the complex behavior of fluid dispensing and high degree of uncertainty associated with the process in a real world environment. In this project, Takagi-Sugeno neural fuzzy systems, is introduced to model the fluid dispensing process for microchip encapsulation. Two process models were generated for the two quality characteristics; encapsulation weight and encapsulation thickness, respectively. Validation tests were performed. The test results were compared with approaches based on statistical regression, neural network and fuzzy regression. From a comparison of the results, it can be concluded that among these the TS neural fuzzy system is the best approach for modeling fluid dispensing.
机译:在半导体制造业中,流体分配是一种流行的工艺,通常用于芯片键合以及电子封装的微芯片封装中。对流体分配过程进行建模很重要,因为它使我们能够了解过程行为,并确定过程的最佳操作条件,以实现高产量,低成本和稳定运行。先前对流体分配的研究主要集中在分析模型的开发上。但是,由于流体分配的复杂行为以及在现实环境中与过程相关的高度不确定性,很难提供可提供准确结果的流体分配分析模型。在该项目中,引入了Takagi-Sugeno神经模糊系统,以对用于微芯片封装的流体分配过程进行建模。针对两个质量特征生成了两个过程模型。封装重量和封装厚度。进行验证测试。将测试结果与基于统计回归,神经网络和模糊回归的方法进行了比较。通过比较结果,可以得出结论,在其中TS神经模糊系统是建模流体分配的最佳方法。

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