Learning Assisted Side Channel Delay Test for Detection of Recycled ICs

TL;DR

This paper introduces a neural network-based delay side-channel testing method to detect recycled ICs, overcoming challenges posed by process variations and the lack of a golden reference.

Contribution

It proposes a novel detection scheme using design features and delay measurements with neural networks to identify recycled ICs without needing a golden chip.

Findings

Effective detection of recycled ICs demonstrated

Delay deviations reliably indicate recycling status

Method robust against process variations

Abstract

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.