Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE open journal of circuits and systems Pub Date : 2023-01-01 DOI:10.1109/OJCAS.2022.3232638
Hendrik P. Nel;Fortunato Carlos Dualibe;Tinus Stander
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Abstract

Oscillation-based testing (OBT) and Oscillation-based built-in self-testing (OBIST) circuits enable detection of catastrophic faults in analogue and RF circuits, but both are sensitive to process, voltage and temperature (PVT) variation. This paper investigates 15 OBT and OBIST feature extraction strategies, and four approaches to threshold selection, by calculating figure-of-merit (FOM) across PVT variation. This is done using a 2.4 GHz LNA in $0.35 \mu \mathrm{m}$ CMOS as DUT. Of the 15 feature extraction approaches, the OBT approaches are found more effective, with some benefit gained from switched-state detection. Of the four approaches to threshold selection (nominal-ranged static thresholds, extreme-range static thresholds, temperature dynamic thresholds, and simple noise-filtered tone detection), dynamic thresholds resulted in the highest average FoM of 0.919, with the best FoM of 0.952, with a corresponding probability of test escape $P\left(T_E\right)$ and yield loss $P\left(Y_L\right)$ of $5 \cdot 10^{-2}$ and $1.89 \cdot 10^{-2}$ respectively but requires accurate temperature measurement. Extreme static threshold selection resulted in a comparable average FoM of 0.912, but with less susceptibility to process variation and without the need for temperature measurement. Binary detection of a noise-filtered oscillating tone is found the least complex approach, with an average FoM of 0.891.
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PVT变化和阈值选择对RFCMOS放大器OBT和OBIST故障检测的影响
基于振荡的测试(OBT)和基于振荡的内置自检(OBIST)电路能够检测模拟和射频电路中的灾难性故障,但两者都对过程、电压和温度(PVT)变化敏感。本文研究了15种OBT和OBIST特征提取策略,以及四种阈值选择方法,通过计算PVT变化的价值图(FOM)。这是使用$0.35 \mu \mathrm{m}$ CMOS中的2.4 GHz LNA作为DUT完成的。在15种特征提取方法中,OBT方法被发现更有效,并且从切换状态检测中获得了一些好处。在四种阈值选择方法(名义范围静态阈值、极端范围静态阈值、温度动态阈值和简单噪声滤波的色调检测)中,动态阈值的平均FoM最高为0.919,最佳FoM为0.952,相应的试验逃逸概率$P\left(T_E\right)$和产量损失$P\left(Y_L\right)$分别为$5 \cdot 10^{-2}$和$1.89 \cdot 10^{-2}$,但需要精确的温度测量。极端静态阈值选择导致平均FoM为0.912,但对工艺变化的敏感性较低,不需要测量温度。对噪声滤波后的振荡音调进行二值检测是最简单的方法,其平均FoM为0.891。
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