Data Mining for Optimizing IC Feature Designs to Enhance Overall Wafer Effectiveness

作者：Chien Chen Fu^*; Hsu Chia Yu

来源：IEEE Transactions on Semiconductor Manufacturing, 2014, 27(1): 71-82.

DOI：10.1109/TSM.2013.2291838

• Data mining
• design for manufacturing
• fab economics
• manufacturing informatics
• manufacturing intelligence
• overall wafer effectiveness
• yield optimization

As global competition continues to strengthen in semiconductor industry, semiconductor companies have to continuously advance manufacturing technology and improve productivity to maintain competitive advantages. Die cost is significantly influenced by wafer productivity that is determined by yield rate and the number of gross dies per wafer. However, little research has been done on design for manufacturing and productivity enhancement through increasing the gross die number per wafer and decreasing the required shot number for exposure. This paper aims to propose a novel approach to improve overall wafer effectiveness via data mining to generate the optimal IC feature designs that can bridge the gap between integrated circuit (IC) design and wafer fabrication by providing chip designer with the optimal IC feature size in the design phase to increase gross dies and reduce the required shots. An empirical study was conducted in a leading semiconductor company for validation. The results have shown that the proposed approach can effectively enhance wafer productivity. Indeed, the developed solution has been implemented in the company to provide desired IC features to IC designers to enhance overall wafer effectiveness.

• 出版日期2014-2
• 单位清华大学