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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