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Methodology of Generating Timing-Slack-Based Cell-Aware Tests

机译:生成基于时序松弛的单元感知测试的方法

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In order to reduce DPPM (defect parts per million), cell-aware (CA) methodology was proposed to cover various types of intra-cell defects. The resulting CA faults can be a 1-time-frame (1tf) or 2-time-frame (2tf) fault, and 2tf CA tests were experimentally verified to be capable of catching a significant number of defective parts not covered by other conventional tests. In this paper, we present a novel methodology for generating 2tf CA tests based on timing slack analysis. The proposed 2tf CA fault model, aware of timing slack and named TS, defines a fault (i) on a cell instance basis, and (ii) based on per-instance timing criticality (according to timing slack). More explicitly, for each cell instance with a specific defect injected, we check its output capacitive load and derive the corresponding extra delay. By comparing the extra delay against timing slack of the cell instance, a delay fault can be defined, and according to its severity, the fault can be further classified into small-delay fault or gross-delay fault. In contrast to prior 2tf CA methodology that is on a cell (rather than cell instance) basis and unaware of timing criticality/slack, our methodology can identify “more realistic” faults which really need to be considered, and potentially the cost/effort for testing those 2tf CA faults can be reduced. Experimental results on a set of 28nm industrial designs demonstrate that, due to more realistic fault identification, the numbers of identified small-delay faults and corresponding test patterns to be applied can be reduced by 35.1% and 24.1% respectively, leading to 40.7% reduction in the runtime of ATPG.
机译:为了减少DPPM(百万分之几的缺陷数),提出了细胞感知(CA)方法来涵盖各种类型的细胞内缺陷。最终的CA故障可能是1时间帧(1tf)或2时间帧(2tf)故障,并且2tf CA测试已通过实验验证,能够捕获大量其他常规测试未涵盖的有缺陷零件。在本文中,我们提出了一种基于时序松弛分析生成2tf CA测试的新颖方法。所提出的2tf CA故障模型了解时序松弛,并命名为TS,它定义了一个故障(i)基于单元实例,以及(ii)基于每个实例的时序临界度(根据时序松弛)。更明确地,对于每个注入特定缺陷的单元实例,我们检查其输出电容负载并得出相应的额外延迟。通过将额外的延迟与单元实例的时序松弛进行比较,可以定义延迟故障,并且根据其严重性,可以将故障进一步分为小延迟故障或总延迟故障。与以前的基于2tf CA的方法(不是基于单元(而不是单元实例))并且不知道时序关键性/松弛性相比,我们的方法可以识别出真正需要考虑的“更实际的”故障,以及潜在的成本/工作量。测试那些2tf CA故障可以减少。在一组28nm工业设计上的实验结果表明,由于更现实的故障识别,已识别的小延迟故障和要应用的相应测试模式的数量可以分别减少35.1%和24.1%,从而减少40.7%。在ATPG的运行时。

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