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Toward a Faster Screening of Faulty Digital Chips via Current-Bound Estimation Based on Device Size and Threshold Voltage

机译:通过基于器件尺寸和阈值电压的电流限制估计来更快地筛选故障数字芯片

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Observations of peak and average currents are important for designed circuits, as faulty circuits have abnormal peaks and average currents. Using current bounds to detect faulty chips is a comparatively innovative idea, and many advanced schemes without them use it as a component in statistical outlier analysis. However, these previous research works have focused on the discussion of the testing impact without a proposed method to define reference current bounds to find faulty chips. A software framework is proposed to synthesize high-performance, power-performance optimized, noise-immune, and low-power circuits with current-bound estimations for testing. This framework offers a rapid methodology to quickly screen potential faulty chips by using the peak and average current bounds for different purposed circuits. The proposed estimation technique generates suitable reference current bounds from transistor threshold voltage and size adjustments. The SPICE-level simulation leads to the most accurate estimations. However, such simulations are not feasible for a large digital circuit. Hence, this work proposes constructing a feasible gate-level software framework for large digital circuits that will serve all of simulation purposes. In comparison with transistor-level Nanosim simulations, the proposed gate-level simulation framework has a margin of error of less than 2% in the peak current, and the computation time is 334 times faster.
机译:峰值电流和平均电流的观察对于设计电路非常重要,因为故障电路具有异常的峰值和平均电流。使用电流边界来检测有缺陷的芯片是一个相对创新的想法,许多没有它们的先进方案都将其用作统计异常分析的组成部分。但是,这些先前的研究工作都集中在对测试影响的讨论上,而没有提出一种定义参考电流范围以找到故障芯片的方法。提出了一种软件框架,以结合电流受限的估计来合成高性能,功率性能优化,抗噪声和低功率电路,以进行测试。该框架提供了一种快速的方法,可以通过针对不同目的电路使用峰值和平均电流范围来快速筛选潜在的故障芯片。所提出的估算技术可根据晶体管阈值电压和尺寸调整来生成合适的参考电流范围。 SPICE级别的仿真导致最准确的估计。但是,这种模拟对于大型数字电路是不可行的。因此,这项工作建议为大型数字电路构建一个可行的门级软件框架,以用于所有仿真目的。与晶体管级Nanosim仿真相比,提出的门级仿真框架在峰值电流中的误差范围小于2%,并且计算时间快了334倍。

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