Logic characterization vehicle design for maximal information extraction for yield learning
Logic characterization vehicle design for maximal information extraction for yield learning
复制标题
用于产量学习的最大信息提取的逻辑表征车辆设计
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Taylor
中科院分区：
文献类型：
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作者：
R. D. Blanton;Ben Niewenhuis;C. Taylor
描述了一种新型的逻辑表征工具（LCV），它可以优化成品率学习的设计、测试和诊断。CM-LCV使用恒定可测性理论和逻辑/布局规则性来创建参数化设计，该设计展示了类似产品的客户设计的前端和后端特性。各种CM-LCV设计（其中一个具有> 4 M门）的设计和测试分析表明，设计时间和密度，测试和诊断都可以同时改善。例如，传统的ATPG产生的测试集是2倍大，对于标准和高级故障模型产生明显较差的故障检测和诊断特性，并且需要比用于生成CM-LCV的恒定测试集的简单方法大几个数量级的运行时间。在设计方面，完全设计的自定义布局比其合成的布局和布线的布局小25%。
A new type of logic characterization vehicle (LCV) that optimizes design, test, and diagnosis for yield learning is described. The Carnegie-Mellon LCV (CM-LCV) uses constant-testability theory and logic/layout regularity to create a parameterized design that exhibits both front- and back-end characteristics of a product-like, customer design. Design and test analysis of various CM-LCV designs (one of which has >4M gates) demonstrates that design time and density, test and diagnosis can all be simultaneously improved. For example, conventional ATPG produces test sets that are 2X larger, produce significantly poorer fault-detection and diagnostic characteristics for standard and advanced fault models, and require runtimes that are several orders of magnitude larger than the simple approach used to generate the constant test set for the CM-LCV. On the design side, a fully designed custom layout is 25% smaller than its synthesized, place-and-routed counterpart.