Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies. In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield. However, the wafer test itself may induce defects to otherwise good dies. Test-induced defects not only hurt overall manufacturing yield but also create problems for yield learning, so the source problems in testing should be identified quickly. In the wafer acceptance test process, dies are probed in a predetermined order, so test-induced defects, also known as site-dependent faults, exhibit specific patterns that can be effectively captured in test paths. In this paper, we analyze characteristics of test-induced defect patterns and define features that can be used by machine learning algorithms for the automatic detection of test-induced defects. Therefore, defective dies caused by the wafer test can be retested for yield improvement. Test data from six real products are used to validate the proposed method. Several machine learning algorithms have been applied, and experimental results show that our method is effective to distinguish between test-induced and fabrication-induced defects. On average, the prediction accuracy is higher than 97%.

中文翻译：

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基于机器学习的晶圆测试缺陷检测方法

晶圆测试是在集成电路（IC）制造之后进行的，以筛选出不良的裸片。另外，该结果可用于识别制造过程中的问题并提高制造成品率。但是，晶圆测试本身可能会导致缺陷，否则会导致良好的管芯。由测试引起的缺陷不仅会损害整体生产良率，而且还会给良率学习带来问题，因此应快速识别测试中的源问题。在晶圆验收测试过程中，以预定的顺序探测管芯，因此测试引起的缺陷（也称为与位置有关的缺陷）表现出可以在测试路径中有效捕获的特定图案。在本文中，我们分析了测试引起的缺陷模式的特征，并定义了可由机器学习算法用于自动检测测试引起的缺陷的特征。因此，可以重新测试由晶片测试引起的有缺陷的管芯，以提高成品率。来自六个真实产品的测试数据用于验证所提出的方法。几种机器学习算法已经被应用，实验结果表明我们的方法可以有效地区分测试引起的缺陷和制造引起的缺陷。平均而言，预测准确性高于97％。实验结果表明，我们的方法可以有效区分测试引起的缺陷和制造引起的缺陷。平均而言，预测准确性高于97％。实验结果表明，我们的方法可以有效区分测试引起的缺陷和制造引起的缺陷。平均而言，预测准确性高于97％。