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Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms

机译:使用神经模糊网络和遗传算法对电子包装液体分配进行建模和优化

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摘要

Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers’ knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation. An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modelling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modelling the fluid dispensing. It is then followed by the determination of process conditions of the process based on a genetic algorithm approach. Validation tests were conducted. Results of them indicate that process conditions determined based on the proposed approaches can achieve the specified quality requirements.
机译:确定微芯片封装的流体分配过程的过程条件是一项非常熟练的任务,通常是基于工程师的知识和通过长期经验获得的直觉,而不是基于理论和分析方法。面对全球竞争,当前的试错法还不够。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。在这项研究中,描述了基于神经模糊网络和遗传算法的流体分配过程的建模和优化。首先,使用神经模糊网络方法对微芯片封装的流体分配过程进行建模。进行了N折验证测试。测试结果表明,在对流体分配进行建模时,基于神经模糊网络方法的建模的平均误差和误差方差均优于其他现有方法(统计回归,模糊回归和神经网络)。然后,基于遗传算法方法确定过程的过程条件。进行了验证测试。结果表明,基于所提出方法确定的工艺条件可以达到规定的质量要求。

著录项

  • 作者

    Chan, KY; Kwong, CK; Tsim, YC;

  • 作者单位
  • 年度 2010
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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