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Defect-Oriented LFSR Reseeding to Target Unmodeled Defects Using Stuck-at Test Sets

机译:使用卡住的测试集将面向缺陷的LFSR重新植入目标未建模的缺陷

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Defect screening is a major challenge for nanoscale CMOS circuits, especially since many defects cannot be accurately modeled using known fault models. The effectiveness of test methods for such circuits can therefore be measured in terms of the coverage obtained for unmodeled faults. In this paper, we present a new defect-oriented dynamic LFSR reseeding technique for test-data compression. The proposed technique is based on a new output-deviation metric for grading stuck-at patterns derived from LFSR seeds. We show that, compared to standard compression-driven dynamic LFSR reseeding and a previously proposed deviation-based method, higher defect coverage is obtained using stuck-at test cubes without any loss of compression.
机译:缺陷筛选是纳米级CMOS电路面临的主要挑战,尤其是因为许多缺陷无法使用已知的故障模型精确建模。因此,可以根据针对未建模故障获得的覆盖范围来衡量此类电路的测试方法的有效性。在本文中,我们提出了一种新的面向缺陷的动态LFSR重播技术,用于测试数据压缩。所提出的技术基于一种新的输出偏差度量,用于对从LFSR种子派生的固定模式进行分级。我们表明,与标准压缩驱动的动态LFSR播种和以前提出的基于偏差的方法相比,使用卡住的测试立方体可获得更高的缺陷覆盖率,而没有任何压缩损失。

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