半導體封裝產業銅焊線製程參數最佳化之研究

Optimization of Parameter Design of Cu Wire Bonding Process in IC Assembly Industry

葉振榮(Cheng-Jung Yeh)

指導教授 ： 李榮貴 ； 蘇朝墩

國立交通大學/管理學院/工業工程與管理系所/博士(2011年)

https://doi.org/10.6842/NCTU.2011.00081

摘要

半導體IC 封裝製造之良率取決於焊線製程。近年來，半導體產業需求輕薄短小的 IC 設計，同時具備高度效能要求之元件，因此黃金焊線品質必須更堅固、細小與結實。然而，令人遺憾的是黃金價格持續攀升，已經變成IC 設計與封裝產業之關鍵議題。此時，銅焊線適時提供一個選擇的解決方案，而依據成本、品質與極度細小之焊墊設計需求，銅線材質特性的表現均優於金線。為了達到最佳的焊線品質，本研究探討影響品質之重要參數，並展開田口方法來最佳化銅焊線製程參數。經由銅焊線技術作業，產品焊線製程良率從98.5% 增加到99.3%,而且藉由良率的增加產生將近70萬美元之收益。同時，在田口方法實施之後，本研究再導入一個結合類神經網路與基因演算法之參數最佳化方法，試圖再度改善與提昇產品焊線製程良率。經實際案例之測試，此一新的方法，使產品焊線製程良率能夠提昇到99.65%，並且節省大約116萬美元之收益。本製程參數最佳化研究係屬多目標回應值，經由望想函數之轉化為單一目標回應值，以作為焊線製程品質之判定依據。

關鍵字

銅焊線、參數設計、田口方法、類神經網路、基因演算法

並列摘要

The yield of IC (Integrated Circuit) assembly manufacturing is dependent on wire bonding. Recently, the semiconductor industry demands smaller IC designs and higher performance requirements. As such, bonding wires must be stronger, finer, and more solid. The cost of gold is continuously appreciating, and this has become a key issue in IC assembly and design. Copper wire bonding is an alternative solution to this problem. It is expected to be superior over Au wires in terms of cost, quality, and fine-pitch bonding pad design. To obtain the best wire bonding quality, this study discussed some important issues on wire bonding quality and employed Taguchi methods in optimizing the Cu wire bonding process. With Cu wire bonding technology, the production yield increased from 98.5% to 99.3 % and brought approximately USD 0.7 million in savings. Meanwhile, a combined approach of neural networks and genetic algorithms after applying the Taguchi methods are used to optimize the Cu wire bonding process. Using this new approach, the production yield increased from 98.5% to 99.65%, resulting in approximately USD 1.16 million in savings. In this study, the response belongs to multiple objectives. In order to judge the wire bonding quality, desirability function is used to transfer multiple objectives to single response.

並列關鍵字

Cu-wire bonding, Parameter design, Taguchi methods, Neural Networks, Genetic Algorithms

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