| Abstract |
Malicious modifi cation of integrated circuits (ICs) in un-
trusted foundry, referred to as "Hardware Trojan", has emerged as a serious security threat. While side-channel analysis has been reported as an eff ective approach to detect hardware Trojans, increasing process variations in nanoscale technologies pose a major challenge, since process noise can easily mask the Trojan e ffect on a measured side-channel
parameter, such as supply current. Besides, existing side-channel approaches suff er from reduced Trojan detection sensitivity with increasing design size. In this paper, we propose a novel scalable side-channel approach, named self-referencing, along with associated vector generation
algorithm to improve the Hardware Trojan detection sensitivity under large process variations. It compares transient current signature of one region of an IC with that of another, thereby nullifying the eff ect of process noise by exploiting spatial correlation across regions in terms of process variations. To amplify the Trojan e ffect on supply current, we propose a
region-based vector generation approach, which divides a circuit-under-test (CUT) into several regions and for each region, finds the test vectors which induce maximum activity in that region, while minimizing the activity in other regions. We show that the proposed side-channel approach
is scalable with respect to both amount of process variations and design size. The approach is validated with both simulation and measurement results using an FPGA-based test setup for large designs including a 32-bit DLX processor core (~ 10^5 transistors). Results shows that our approach can fi nd ultra-small (<0.01% area) Trojans under large process variations of up to +/-20% shift in transistor threshold voltage.
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