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Recycled Analog and Mixed Signal Chip Detection at Zero Cost Using LDO Degradation

机译:利用LDO降级以零成本实现循环模拟和混合信号芯片检测

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Counterfeit electronics impact the global economy and pose life-threatening risks to critical systems and infrastructure. Analog/mixed-signal (AMS) chips are the most widely reported counterfeit chip type, but existing countermeasures are impractical for detecting them. In this paper, we propose a method to detect recycled AMS counterfeits that exploits degradation of power supply rejection ratio (PSRR) in low drop out (LDO) regulators. Our zero cost approach does not require information about the component's design. Moreover, due to the ubiquity of LDOs, it may apply to active and legacy AMS system on chips (SoCs). To evaluate the feasibility and effectiveness of our method, we use an automated test setup to collect PSRR data from commercial off-the-shelf LDOs before and after aging. Machine learning algorithms ranging from unsupervised to supervised are applied to differentiate between aged (i.e., synthetically recycled) and new LDOs. Silicon results confirm that semi-supervised and supervised algorithms are effective even with LDOs used less than 10 days (for 65nm technology node).
机译:假冒电子产品影响全球经济,并对关键系统和基础设施构成威胁生命的风险。模拟/混合信号(AMS)芯片是最广泛报道的伪造芯片类型,但是现有的对策无法检测到它们。在本文中,我们提出了一种检测回收的AMS假冒产品的方法,该方法利用了低压差(LDO)稳压器中电源抑制比(PSRR)的下降。我们的零成本方法不需要有关组件设计的信息。此外,由于LDO的普遍存在,它可能适用于有源和旧式AMS片上系统(SoC)。为了评估我们方法的可行性和有效性,我们使用了自动测试设置,以在老化之前和之后从商业现货LDO收集PSRR数据。应用了从无监督到有监督的机器学习算法,以区分旧的(即合成回收的)LDO和新的LDO。芯片结果证实,即使LDO使用少于10天(对于65nm技术节点),半监督和监督算法仍然有效。

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