基于缺陷聚类的已知好模(KGD)筛选的实验研究

A. Singh, P. Nigh, C. M. Krishna
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引用次数: 39

摘要

长期以来,基于缺陷位置的模具筛选在工业中一直非正式地实行,即晶圆片或晶圆片部分显示高缺陷水平的骰子被丢弃。最近,这种方法得到了改进,以便在评估特定模具的测试结果时也考虑晶圆上邻近骰子的测试结果。原则上,对晶圆上的缺陷分布使用负二项统计,这种方法可以更好地优化测试成本,并在裸晶片和封装芯片中筛选低缺陷水平。在本文中,我们首次提出了实验测试数据来证明这种新方法的有效性。我们的结果是基于对来自IBM工艺的23个晶圆上的4784个骰子的广泛测试。我们表明,基于缺陷聚类考虑的裸晶片筛选可以显着降低通过晶圆探针测试的晶片的缺陷水平。这种方法也有可能筛除老化故障。因此,它提供了新的低成本策略,为MCM应用提供高质量的“已知好的”模具(KGD)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening for known good die (KGD) based on defect clustering: an experimental study
Die screening based on the locality of defects has long been informally practised in the industry whereby dice from wafers, or parts of the wafer, that display high defect levels are discarded. More recently this approach has been refined such that test results for neighbouring dice on the wafer are also considered in evaluating test results for a particular die. It has been shown in principle, using negative binomial statistics for defect distributions on wafers, that such an approach can much better optimize test costs and screen for low defect levels in bare dice and packaged chips. In this paper we present, for the first time, experimental test data to demonstrate the effectiveness of this new approach. Our results are based on extensive testing of 4784 dice on 23 wafers from an IBM process. We show that bare die screening based on defect clustering considerations can significantly reduce defect levels in dice that pass wafer probe tests. This approach also has the potential to screen out burn-in failures. Thus it offers new low cost strategies for delivering high quality "known- good" die (KGD) for MCM applications.
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