Learning Assisted Side Channel Delay Test for Detection of Recycled ICs
Learning Assisted Side Channel Delay Test for Detection of Recycled ICs
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用于检测回收 IC 的学习辅助侧通道延迟测试
DOI：
10.1145/3394885.3431640
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Karimi, Naghmeh
中科院分区：
文献类型：
--
作者：
Vakil, Ashkan;Niknia, Farzad;Mirzaeian, Ali;Sasan, Avesta;Karimi, Naghmeh
随着设计流程的外包，确保集成电路的安全性和可信性变得更具挑战性。在安全威胁中，IC伪造和回收IC由于其质量低劣而受到了广泛关注，反过来，它们对底层设备的可靠性和安全性产生了负面影响。由于在芯片制造期间发生的工艺变化和工艺漂移的影响，检测回收的IC具有挑战性。此外，依靠黄金筹码作为比较的基础并不总是可行的。因此，本文提出了一种基于延迟边道测试的IC回收检测方案。所提出的方法依赖于在设计流程中提取的特征和从目标芯片中提取的样本延迟来构建神经网络模型，使用该神经网络模型可以真正识别目标芯片是新的还是回收的。所提出的方法分类的目标芯片的时序路径分为两组，根据他们的脆弱性老化使用从设计中收集的信息，并检测回收的IC的基础上，这两组彼此的延迟的偏差。
With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of attention due to their inferior quality, and in turn, their negative impact on the reliability and security of the underlying devices. Detecting recycled ICs is challenging due to the effect of process variations and process drift occurring during the chip fabrication. Moreover, relying on a golden chip as a basis for comparison is not always feasible. Accordingly, this paper presents a recycled IC detection scheme based on delay side-channel testing. The proposed method relies on the features extracted during the design flow and the sample delays extracted from the target chip to build a Neural Network model using which the target chip can be truly identified as new or recycled. The proposed method classifies the timing paths of the target chip into two groups based on their vulnerability to aging using the information collected from the design and detects the recycled ICs based on the deviation of the delay of these two sets from each other.
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