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An Adaptive Approach to Minimize System Level Tests Targeting Low Voltage DVFS Failures

机译:最小化靶向低压DVFS故障的系统级测试的自适应方法

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Traditional low cost scan based structural tests no longer suffice for delivering acceptable defect levels in many processor SOCs, especially those targeting low power applications. Expensive functional system level tests (SLTs) have become an additional and necessary final test screen. Efforts to eliminate or minimize the use of SLTs have focused on new fault models and improved test generation methods to improve the effectiveness of scan tests. In this paper we argue that given the limitations of scan timing tests, such an approach may not be sufficient to detect all the low voltage failures caused by circuit timing variability that appear to dominate SLT fallout. Instead, we propose an alternate approach for meaningful cost savings that adaptively avoids SLT tests for a subset of the manufactured parts. This is achieved by using parametric and scan tests results from earlier in the test flow to identify low delay variability parts that can avoid SLT with minimal impact on DPPM. Extensive SPICE simulations support the viability of our proposed approach. We also show that such an adaptive test flow is also very well suited to real time optimization during the using machine-learning techniques.
机译:传统的低成本扫描基于结构测试不再足以在许多处理器SOC中提供可接受的缺陷级别,尤其是瞄准低功率应用的结构。昂贵的功能系统级测试(SLT)已成为另一个并必要的最终测试屏幕。消除或最小化SLT的努力集中在新的故障模型和改进的测试生成方法上,以提高扫描测试的有效性。在本文中,我们认为,鉴于扫描时序测试的局限性,这种方法可能不足以检测由电路定时变化引起的所有低压故障,这些都是似乎占据主导地位的SLT辐射。相反,我们提出了一种用于有意义的成本节省的替代方法,可自适应地避免制造部件的子集的SLT测试。这是通过使用参数和扫描测试来实现的,从测试流程中的早期结果识别可以避免SLT的低延迟可变性部件,这对DPPM产生最小的影响。广泛的Spice模拟支持我们提出的方法的可行性。我们还表明,这种自适应测试流程在使用机器学习技术期间也非常适合于实时优化。

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