Abstract

Portable devices with multiple functions are commonly used in modern life, and the dies in these products have become much thinner and slimmer over time. Due to the rapid advancements in manufacturing technology in the semi-conductor industry, the process yield requirements grow increasingly strict. In advanced packaging manufacturing, the process is usually with multiple lines and often requires a very low fraction of defective. To assess the manufacturing yield precisely, the process capability index is widely used. $C_{pk}^M$ is a generalization yield index designated for measuring the yield of multi-line processes. However, the typical existing method for obtaining the lower confidence bound of $C_{pk}^M$ is conservative and it may mislead managers into making incorrect decisions. In this study, the nonparametric and parametric standard bootstrap methods have been implemented to establish a reliable and improved yield assessment. Three methods are investigated and the power comparison is provided. Then, two effective transformation methods to handle non-normal processes are presented and one example with simulation data is given for algorithm demonstration. Finally, an application of yield assessment for underfill processes with two manufacturing lines is presented. The simulation results demonstrate that based on the proposed method, we can reliably evaluate the true manufacturing yield and make a more powerful decision.

摘要

现代生活中普遍使用具有多种功能的便携式设备，随着时间的推移，这些产品中的芯片变得更薄更薄。由于半导体行业制造技术的快速进步，对工艺良率的要求越来越严格。在先进的封装制造中，该过程通常具有多条生产线，并且通常需要非常低的缺陷率。为了准确评估制造良率，广泛使用工艺能力指数。$C_{p克}^{米}$是一个泛化良率指数，用于衡量多线工艺的良率。然而，现有的典型的获得置信下界的方法$C_{p克}^{米}$是保守的，它可能会误导管理者做出错误的决定。在本研究中，已实施非参数和参数标准自举方法，以建立可靠且改进的良率评估。研究了三种方法并提供了功率比较。然后，提出了两种处理非正态过程的有效变换方法，并给出了一个带有仿真数据的例子进行算法演示。最后，介绍了具有两条生产线的底部填充工艺的良率评估应用。仿真结果表明，基于所提出的方法，我们可以可靠地评估真实的制造良率并做出更强大的决策。