Wafer-Level Characteristic Variation Modeling Considering Systematic Discontinuous Effects
Wafer-Level Characteristic Variation Modeling Considering Systematic Discontinuous Effects
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DOI：
10.1145/3566097.3567915
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发表时间：
2023-01
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影响因子：
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通讯作者：
Takuma Nagao;Tomoki Nakamura;Masuo Kajiyama;Makoto Eiki;M. Inoue;Michihiro Shintani
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文献类型：
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作者：
Takuma Nagao;Tomoki Nakamura;Masuo Kajiyama;Makoto Eiki;M. Inoue;Michihiro Shintani
在大规模集成电路（LSI）测试中，圆片级误差统计建模是一种在保证测试质量的同时降低测试成本的有效方法。在该方法中，从几个测量的LSI电路中统计地预测在晶片上制造的未测量的LSI电路的性能。传统的统计方法对晶片中的空间平滑变化进行建模。然而，实际的晶片可能具有由制造环境系统地引起的不连续变化，诸如发射依赖性。在这项研究中，我们提出了一种建模方法，认为不连续的变化，在晶圆characteristics应用知识的制造工程师的模型估计使用高斯过程回归。在所提出的方法中，过程变量被分解为系统的不连续和全局分量，以提高估计精度。使用工业生产测试数据集进行的评估表明，所提出的方法减少了超过33%的传统方法相比，整个晶圆的估计误差。
Statistical wafer-level variation modeling is an attractive method for reducing the measurement cost in large-scale integrated circuit (LSI) testing while maintaining the test quality. In this method, the performance of unmeasured LSI circuits manufactured on a wafer is statistically predicted from a few measured LSI circuits. Conventional statistical methods model spatially smooth variations in wafer. However, actual wafers may have discontinuous variations that are systematically caused by the manufacturing environments, such as shot dependence. In this study, we propose a modeling method that considers discontinuous variations in wafer charac-teristics by applying the knowledge of manufacturing engineers to a model estimated using Gaussian process regression. In the proposed method, the process variation is decomposed into the systematic discontinuous and global components to improve the estimation accuracy. An evaluation performed using an industrial production test dataset shows that the proposed method reduces the estimation error for an entire wafer by over 33% compared to conventional methods.