Key research themes
1. How can test pattern generation and side-channel analysis be optimized to effectively detect hardware Trojans with rare activation conditions?
This research theme focuses on developing test generation methods that improve the likelihood of activating and detecting hardware Trojans (HTs), which are often designed to trigger under very rare internal signal conditions. It emphasizes enhancing side-channel analysis techniques by generating input patterns that maximize switching activity in rare nodes or circuits prone to HT insertion, thereby amplifying power or timing anomalies distinguishable from process variation. This area is crucial because small-sized, stealthy HTs remain dormant under conventional testing, impeding their detection.
2. What strategies combine design-for-trust approaches and detection frameworks to mitigate hardware Trojans in third-party designs and complex supply chains?
This theme investigates methodologies that integrate design-time preventive techniques such as logic locking, design-for-trust insertions, and post-manufacturing verification to reduce the risk posed by hardware Trojans in outsourced, third-party intellectual property (IP) cores and complex supply chains. It includes both formal and statistical verification frameworks, feature extraction and multi-level analysis for IP trust verification, and methods for enhancing Trojan detectability through structural modifications. The goal is a scalable, efficient framework to ensure trustworthiness and detect stealthy Trojans in real-world IP reuse scenarios.
3. How can hardware Trojan detection be enhanced by hardware reverse engineering, optical imaging, and machine learning approaches across different abstraction levels?
This theme explores advanced hardware Trojan detection by employing reverse engineering workflows, non-destructive backside optical watermark imaging, and machine learning models applied at Register Transfer Level (RTL), gate-level netlists, and physical layouts. The focus is on practical, scalable detection methods that do not require Golden chips or full netlist extraction, improve detection resolution against small Trojans, and interpret complex feature sets for trustworthy decision-making. These approaches address the physical and design-level verification challenges and utilize statistical and explainable AI techniques to improve accuracy and explainability in Trojan detection.