Impact of multiple-detect test patterns on product quality
Impact of multiple-detect test patterns on product quality
复制标题
多重检测测试模式对产品质量的影响
DOI：
10.1109/test.2003.1271091
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
J. Rajski
中科院分区：
文献类型：
--
作者：
B. Benware;C. Schuermyer;S. Ranganathan;R. Madge;P. Krishnamurthy;Nagesh Tamarapalli;Kun;J. Rajski
摘要本文介绍了多重检测测试模式对出厂产品质量的影响。它引入了一个ATPG工具，生成多检测测试模式，同时最大限度地覆盖节点到节点桥接缺陷。通过使用这些新的多重检测模式测试生产ASIC获得的体积数据显示出增强的缺陷筛选能力，并且与ATPG工具估计的桥接覆盖率非常一致。1.引言制造测试的关键目标之一是确保高质量的运输部件，同时管理测试成本。基于扫描的DFT方法，结合ATPG工具，自动生成具有非常高的故障覆盖率的测试模式。基于结构的ATPG工具的优点是它可以针对不同的故障模型，如固定、转换、路径延迟和DDQ，高效地生成测试集。DFT工具通过报告目标故障模型的故障覆盖率来评估测试模式的质量。然而，真实的缺陷可能并不总是被针对目标故障模型生成的测试模式检测到。固定型故障模型因斯一开始就被用于DFT中，虽然显示出一些局限性和不完善性，但它已经证明了其鲁棒性和适应性。即使固定故障模型可能并不总是对故障电路的行为进行建模，但它可以很好地用作目标，即，为测试固定故障而开发的测试集也将覆盖许多其他不表现为固定故障的缺陷。对桥接缺陷的良好理解是解释固定故障模型有效性的核心。它还提供了其增强功能的关键线索。在一项对最先进的微处理器设计中的桥接故障进行的实验研究中[1]，已经观察到大约80%的桥接发生在节点和电源或Vss之间，20%涉及非电源节点。70%的缺陷涉及全局信号，而叶级信号仅占30%。在另一项桥接缺陷扫描测试的实验评估[2]中，得出的结论是，具有电源轨的桥接占所有桥接缺陷的60%至90%。很明显，检测节点上的固定故障的测试将检测电源线的低电阻桥接缺陷。这正是节点stuckat- -0或stuckat--1的行为。然而，不能保证检测到节点到节点桥接缺陷。如果一个节点上的stuckat故障被检测到一次，则检测到另一个信号概率为50%的不相关节点的静态桥接故障的概率f也为50% [3]。如果检测到stuckat故障两次，则检测到另一个节点作为攻击者的桥接故障的估计概率为75%。信号相关性可以减少节点到节点桥接故障的覆盖。据观察[1]，具有大于95%固定故障覆盖率的测试集仅产生33%的节点到节点桥接故障覆盖率。最有可能的是，令人失望的覆盖率是信号相关性的一个伪影。典型地，由针对单次检测的常规ATPG创建的测试集可以具有仅检测一次的高达6%的故障和仅检测一次或两次的高达10%的故障。这可能导致节点到节点桥接缺陷的覆盖不足。总的来说，克服这一局限性和提高测试质量有两个方向。一个方向是通过描述缺陷行为并将其以合适的形式呈现给ATPG工具来增强故障模型。在这种情况下，故障模型更加精确和复杂，故障列表更长。先进的故障模型，如桥接故障和串扰效应，使用物理布局信息来编译故障列表。这种方法的一个完整例子在[2]中得到了证明。在这里，可能的桥梁被确定的布局分析使用加权临界面积和他们的行为是由不同类型的故障和一个特殊的网表建模。该项目的实验结果表明，
Abstract This paper presents the impact of multiple-detect test patterns on outgoing product quality. It introduces an ATPG tool that generates multiple-detect test patterns while maximizing the coverage of node-to-node bridging defects. Volumedata obtained by testing a production ASIC with these new multiple-detect patterns shows increased defect screening capability and very good agreement with the bridging coverage estimated by the ATPG tool. 1. Introduction One of the key objectives of manufacturing test is to ensure high quality of shipped parts while managing the cost of test. Scan–based DFT methodology, combined with ATPG tools, automate the generation of test patterns with very high fault coverage. The advantage ofa structure-based ATPG tool is its high efficiency and effectiveness in generating a test set by targeting different fault models, such as stuck-at, transition, path delay, and DDQ . DFT tooI ls assess the quality of test patterns by reporting the fault coverage of the target fault models. However, real defects may not always be detected by test patterns generated for the targeted fault model. The stuck-at fault model has been used in DFT ince sthe very beginning and, while showing some limitations and imperfections, it has demonstrated its robustness and adaptability. Even though the stuck-at fault model may not always model behavior of a faulty circuit it serves very well as a target, i.e. a test set developed to test stuck-at faults will also cover many other defects that do not behave as stuck-at faults. Good understanding of bridging defects is at the center of explanation of the effectiveness of the stuck-at fault model. It also provides the key clues to its enhancements. In an experimental study of bridging faults in a state of the art microprocessor design [1] it has been observed that approximately 80% of all bridges occur between a node and Vcc or Vss, and 20% involve nonsupply nod- es. Global signals were involved in 70% of these defects and leaf-level signals contributed only 30%. In another experimental evaluation of scan tests for bridging defects [2] it was concluded that bridges with power rails contributed between 60% to 90% of all bridging defects. It is clear that a test that detect a stuck-at fault on a node willdetect a low resistive bridging defect with the supply lines. This is exactly the behavior of a node stuckat- -0 or stuckat- -1. However, the detection of node-to-node bridging defects is not guaranteed. If a stuckat fault on a node is detected once, the- probability f detecting a static bridging fault witho another un-correlated node that has signal probability 50% is also 50% [3].If the stuck at fault is detected- twice, the estimated probability of detecting the bridging fault with another node acting as an aggressor is 75%. Signal correlation may reduce the coverage of nodeto-node bridging faults. It was- observed [1] that a test set with greater than 95% stuck-at fault coverage produced only 33% coverage of nodeto-node bridging faults. Most likely the- disappointing coverage was an artifact of signal correlation. Typically a test set created by conventional ATPG aiming at single detection may have up to 6% of faults detected only once and up to 10% of faults detected only once or twice. This may result in inadequate coverage of nodeto-node -bridging defects. In general, there are two directions to overcome the limitation and improve the test quality. One direction is to enhance the fault model by describing the defect behavior and presenting it in a suitable form to the ATPG tool. In this case the fault model is more precise and complex and the fault list s longer. Thei advanced fault models, like bridging faults and cross-talk effects, use physical layout information to compile the fault lists. A complete example of this approach is demonstrated in [2]. Here the possible bridges are identified by analysis of layout using weighted critical area and their behavior is modeled by different types of faults and a special netlist. The experimental results from the project show that