Quantified contribution of design for manufacturing to yield at 28nm

T. Herrmann, S. Malik, S. Madhavan
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引用次数: 1

Abstract

Yield is the single most important criterion which drives the economics of our industry, impacting the bottom line directly. It is a well understood fact that both foundries and fabless companies have an extremely strong interest in achieving high yield as quickly as possible to meet the economies of scale and rapid time to market. At the 28nm node and below, implementation of DFM is believed to be particularly critical to enable a fast yield ramp. Quantification of the yield impact of various DFM enhancements is crucial to drive the appropriate design tradeoffs. In this paper we present an analysis of yield impact of DFM features over the duration of technology and product yield ramp for the 28nm node. Yield has inherent variation due to nature of its dependency on multiple factors and stages which makes it difficult to attribute yield signal to a small action in a long chain of event, from design to fabrication, leading to successful yield. We created a set of designs in 28nm, with and without DFM, where DFM changes were done only opportunistically. After finishing these designs, both the unmodified and the DFM enhanced layouts were placed side by side on the test chip reticles. Both instances got tested over long time for yield evaluation on silicon to create enormous amount of data which we analyzed and present in this paper. For analysis of all this data, we compare different statistical methods to understand the same and present challenges faced using these methods. We conclude with successful application of Matched Pair statistical method that quantified yield sensitivity to the DFM design changes.
量化设计对28nm制程产率的贡献
产量是推动我们行业经济发展的最重要的标准,直接影响到底线。众所周知,晶圆代工厂和无晶圆厂公司都对尽快实现高产量有极大的兴趣,以满足规模经济和快速上市。在28nm及以下节点,DFM的实施被认为是实现快速产量增长的关键。量化各种DFM增强对产量的影响对于驱动适当的设计权衡至关重要。在本文中,我们分析了DFM特征在28nm节点的技术和产品良率斜坡期间的良率影响。由于其依赖于多种因素和阶段的性质,良率具有固有的变化,这使得很难将良率信号归因于从设计到制造的长链事件中的一个小动作,从而导致成功的良率。我们创建了一组28nm的设计,有和没有DFM, DFM的变化只是偶然的。在完成这些设计后,将未经修改的布局和DFM增强的布局并排放置在测试芯片上。这两个实例都经过了长时间的测试,以评估硅的成品率,从而产生了大量的数据,我们对这些数据进行了分析并在本文中提出。为了分析所有这些数据,我们比较了不同的统计方法,以了解使用这些方法所面临的相同和当前的挑战。最后,我们成功地应用了匹配对统计方法,量化了产量对DFM设计变化的敏感性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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