Robust Reconfigurable Scan Networks

Robust Reconfigurable Scan Networks
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强大的可重构扫描网络

DOI:
10.23919/date54114.2022.9774770
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发表时间:
2022
期刊:
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
影响因子:
--
通讯作者:
H. Wunderlich
H. Wunderlich
中科院分区:
--
文献类型:
--
作者:
N. Lylina;Chih;H. Wunderlich

文献摘要

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可重构扫描网络(RSN)可访问嵌入式仪器的评估结果,并在整个设备生命周期内控制其运行。同时,RSN中的单个故障可能会大大降低仪器的可访问性。在硅后验证期间,它可能会阻止从器件中提取完整数据。在联机操作期间,通过缺陷RSN无法访问运行时关键仪器可能最终导致系统故障。本文通过提出鲁棒的RSN来解决上述两种情况。我们表明,通过使少量精心挑选的点在RSN的更强大,整个访问机制变得更加可靠。一个灵活的成本函数评估的重要性,具体的控制原语的整体可访问性的仪器。根据成本函数,最小数量的点被硬化以防止永久性故障。即使存在缺陷,也可以通过所产生的RSN访问所有关键仪器以及大多数剩余仪器。与现有的容错RSN相比,该方案不改变RSN的拓扑结构,并且需要较少的硬件开销。选择性强化是一个多目标优化问题,并使用进化算法求解。实验结果验证了该方法的有效性和可扩展性。
Reconfigurable Scan Networks (RSNs) access the evaluation results from embedded instruments and control their operation throughout the device lifetime. At the same time, a single fault in an RSN may dramatically reduce the accessibility of the instruments. During post-silicon validation, it may prevent extracting the complete data from a device. During online operation, the inaccessibility of runtime-critical instruments via a defect RSN may eventually result in a system failure. This paper addresses both scenarios above by presenting robust RSNs. We show that by making a small number of carefully selected spots in RSN s more robust, the entire access mechanism becomes significantly more reliable. A flexible cost function assesses the importance of specific control primitives for the overall accessibility of the instruments. Following the cost function, a minimized number of spots is hardened against permanent faults. All the critical instruments as well as most of the remaining instruments are accessible through the resulting RSNs even in the presence of defects. In contrast to state-of-the-art fault-tolerant RSNs, the presented scheme does not change the RSN topology and needs less hardware overhead. Selective hard-ening is formulated as a multi-objective optimization problem and solved by using an evolutionary algorithm. The experimental results validate the efficiency and the scalability of the approach.