基于人工智能的扫描矢量重排序捕获功率最小化

U. Mehta, K. Dasgupta, N. Devashrayee, H. Parmar
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引用次数: 6

摘要

测试功率是基于IP核的SoC外部测试的主要问题。在外部测试期间,从大量用于减少开关活动的各种可用技术中,只有那些不需要对内部结构进行任何修改并且不需要使用任何测试开发工具的方案,如“不关心位填充”和“重新排序”,用于包含隐藏结构的IP内核的SoC。测试载体的序列对捕获能力有重要影响。触发器在捕获期间的状态变化取决于该触发器在当前扫描输出矢量和下一个扫描输入矢量中的状态。本文提出了一种基于人工智能的扫描向量重排序(ASVR)方法来优化捕获功率降低。该方法使用非常流行的A *算法来重新排序测试向量,以最小化捕获操作期间的切换活动。
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Artificial intelligence based scan vector reordering for capture power minimization
Test Power is the major issues for the external testing of IP core based SoC. From a large pool of diverse available techniques for switching activity reduction during the external testing, only those schemes like ‘don't care bit filling’ and ‘reordering’ which do not require any modification in internal structure and do not demand use of any test development tool is used for SoC containing IP cores with hidden structure. The sequence of test vectors plays a significant role in capture power. The change in state of flipflop during capture depends upon the states of that flipflop in current scan-out vector and next scan-in vector. In this paper, the Artificial Intelligence Based Scan Vector Reordering (ASVR) is proposed to optimize the capture power reduction. This method uses very popular A∗ algorithm to reorder the test vectors to minimize the switching activity during capture operation.
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