题名 |
Improving the Plastic Ball Grid Array Assembly Yield: A Case Study |
并列篇名 |
Plastic Ball Grid Arrays電漿清洗製程良率改善之研究 |
DOI |
10.29977/JCIIE.200607.0005 |
作者 |
洪永祥(Y. H. Hung);黃美玲( M. L. Huang) |
关键词 |
PGBA組裝 ; 電漿清洗 ; 類神經網路 ; 基因演算法 ; PBGA assembly ; Plasma cleaning ; neural network ; genetic algorithm |
期刊名称 |
工業工程學刊 |
卷期/出版年月 |
23卷4期(2006 / 07 / 01) |
页次 |
311 - 318 |
内容语文 |
英文 |
中文摘要 |
在半導體封裝PBGA製程中,電漿清洗是必要製程。主要負責封裝過程中所產生的污染源。特別在wire bonding及modling製程之前,若存在任何微小的有機污染物質,會使後製程組裝的不良率大幅提高。依據工廠數據顯示高階PBGA的重工成本高達3美金。因此之故,如何提升PBGA的製程良率降低生產成本,對封裝廠商而言是非常重要的品質課題。本研究,主要結合田口實驗方法及BPNN(back-propagation neural networks)神經網路與基因演算法(genetic algorithms)找尋最佳化的電漿清洗製程參數,藉以提升PGBA 的電漿清洗(plasma cleaning processes)製程品質與良率。 |
英文摘要 |
The plasma cleaning process cleaning the residual containments in the assembly processes is one of the major processes in the plastic ball grid arrays (PBGA) assembly processes. Especially, when there is any micron particle existed before wire bonding and molding, parts of nonconforming will increase. With the rapid growth in production, yield improvement to reduce the production cost is essential for PBGA industry. In fact, the plasma cleaning process before wire bonding and molding both enhances the PBGA yield and reduces the production cost effectively. This study combines neural network with Taguchi method to structure a well-trained prediction model, further searches for the optimal plasma cleaning parameter design through genetic algorithm, and finally enhances the process yield and production quality in a central Taiwan PBGA company. |
主题分类 |
工程學 >
工程學總論 |
参考文献 |
|