Combining radial basis function neural network and genetic algorithm to improve HDD driver IC chip scale package assembly yield

M.L. Huang; Y.H. Hung

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Combining radial basis function neural network and genetic algorithm to improve HDD driver IC chip scale package assembly yield

机译：结合径向基函数神经网络和遗传算法提高HDD驱动器IC芯片规模封装的组装良率

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In recent years, the future trend of micro HDD driver IC for large capacity micro HDD is to become lighter, thinner, shorter and smaller. Among all the options available for micro HDD driver IC's assembly, warpage is an important issue related to micro HDD driver IC manufacturability and reliability. The optimal packaging manufacturing process for driver IC for micro HDD is chip scale package (CSP). However, the production and assemble process for CSP is much more difficult. The aim of this study is to improve the lower warpage properties for 0.65 mm CSP assembly yield using a model based on a radial basis function network (RBFN), and the optimal HDD packaging process parameter design is achieved through a genetic algorithm (GA).

机译：近年来，用于大容量微型HDD的微型HDD驱动器IC的未来趋势是变得更轻，更薄，更短和更小。在微型HDD驱动器IC的所有可用选件中，翘曲是与微型HDD驱动器IC的可制造性和可靠性有关的重要问题。微型HDD驱动器IC的最佳封装制造工艺是芯片级封装（CSP）。但是，CSP的生产和组装过程要困难得多。这项研究的目的是使用基于径向基函数网络（RBFN）的模型来提高0.65 mm CSP组件产量的较低翘曲性能，并通过遗传算法（GA）实现最佳的HDD封装工艺参数设计。

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《Expert systems with applications》 |2008年第1期|p.588-595|共8页
作者
M.L. Huang; Y.H. Hung;
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正文语种 eng
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Combining radial basis function neural network and genetic algorithm to improve HDD driver IC chip scale package assembly yield

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