Taming Combinational Trojan Detection Challenges with Self-Referencing Adaptive Test Patterns

Abstract

While many side-channel methods have been proposed for detecting hardware Trojans inserted by an untrusted foundry, they are challenged in the face of process variation noise. The impacts of process variation have forced researchers to propose costly design enhancements to improve detection as a counter to the deficiency of current easy-to-implement test pattern-based methods. To overcome process variation noise with no design cost, we propose a novel self-referencing adaptive approach based on test pattern construction, which learns from and conforms to device characteristics to maximally magnify the Trojan signal. Through iterative test pattern modifications, response analyses, and decision-making, we can pursue suspicious behaviors and increase the likelihood of Trojan detection. Experiments on Trust-Hub Trojan circuit benchmarks show the efficacy of this technique, magnifying an equivocal starting signal 22 to 130 to deliver crisp resolution to the question of Trojan existence.