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Statistical Models of Overdispersed Spatial Defects for Predicting the Yield of Integrated Circuits
IEEE Transactions on Reliability ( IF 5.9 ) Pub Date : 2020-06-01 , DOI: 10.1109/tr.2019.2943925
Tao Yuan , Suk Joo Bae , Yue Kuo

Defects generated in semiconductor manufacturing processes have serious effects on the yield of integrated circuits (ICs). Accurate modeling of the defect counts on IC chips is crucial for predicting the yield. The conventional Poisson yield model tends to underestimate the true yield by ignoring overdispersed patterns of defects on the wafer. This article uses various models based on the generalized Poisson (GP) distribution and/or HZ distributions to explore the overdispersed defect counts on semiconductor wafers. Real wafer map data are used to compare the performance of both nonregression and regression modeling approaches in terms of the log-likelihood, AIC, and relative bias for yield estimation. Analytical results indicate that the GP distribution is a competitive alternative to the negative binomial (NB) distribution for modeling defect counts on IC chips because the GP distribution can model overdispersion, underdispersion, or no dispersion. In particular, HZ models based on the NB and GP distributions show good potential for predicting the yield of IC chips on wafers.

中文翻译:

用于预测集成电路良率的过度分散空间缺陷的统计模型

半导体制造过程中产生的缺陷对集成电路(IC)的良率有严重影响。IC 芯片缺陷计数的准确建模对于预测良率至关重要。传统的泊松良率模型倾向于通过忽略晶片上过度分散的缺陷模式来低估真实良率。本文使用基于广义泊松 (GP) 分布和/或 HZ 分布的各种模型来探索半导体晶片上的过度分散缺陷计数。真实晶圆图数据用于在对数似然、AIC 和产量估计的相对偏差方面比较非回归和回归建模方法的性能。分析结果表明,GP 分布是负二项式 (NB) 分布的竞争替代方案,用于对 IC 芯片上的缺陷计数进行建模,因为 GP 分布可以对过度分散、欠分散或无分散进行建模。特别是,基于 NB 和 GP 分布的 HZ 模型显示出预测晶圆上 IC 芯片良率的良好潜力。
更新日期:2020-06-01
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