Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017–2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.

At S&P 2023, Puschner et al. made a valuable dataset for hardware Trojan detection research publicly available. It contains a complete set of Scanning Electron Microscope (SEM) images of four different digital Integrated Circuits (ICs) fabricated at progressively smaller semiconductor technology nodes. Puschner et al. reported preliminary evidence that feature sizes affect Trojan detection performance, but they were unable to disentangle effects caused by insertion strategies or by degrading image quality from those intrinsic to the underlying standard cell libraries. Distinguishing those causes, however, is crucial to understand whether improved tooling (e.g., higher resolution imaging equipment) can remove the observed technology bias, or whether susceptibility to stealthy hardware Trojans is indeed an inherent property of a cell library. In this work, we dive deep into the S&P 2023 dataset to answer these questions. We devise alternative metrics to those of Puschner et al., in order to assess and compare the potential susceptibility of standard cell libraries more meaningfully. We find clear differences between the evaluated process nodes. However, in all cases we identify cells that implement distinct logic functions yet are visually indistinguishable in backside SEM images. We exploit this property to construct stealthy, standard-cell-based hardware Trojans and present a concrete case study: a privilege-escalation backdoor in an Ibex RISCV core. Our results demonstrate that cell libraries can - and should - be evaluated for their potential "Trojanizability", and we recommend practical defenses.

Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits.Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities.In this work, we introduce ReverSim, a software environment that models key HRE subprocesses and integrates standardized cognitive tests.ReverSim enables quantitative studies with easier-to-recruit non-experts to uncover cognitive factors relevant to HRE.We empirically evaluated ReverSim in three studies.Semi-structured interviews with 14 HRE professionals confirmed its comparability to real-world HRE processes.Two online user studies with 170 novices and intermediates revealed effective differentiation of participant performance across a spectrum of difficulties, and correlations between participants' cognitive processing speed and task performance.ReverSim is available as open-source software, providing a robust platform for controlled experiments to assess cognitive processes in HRE, potentially opening new avenues for hardware protection.

The increasing importance of supply chain security for digital devices---from consumer electronics to critical infrastructure---has created a high demand for skilled cybersecurity experts. These experts use Hardware Reverse Engineering (HRE) as a crucial technique to ensure trust in digital semiconductors. Recently, the US and EU have provided substantial funding to educate this cybersecurity-ready semiconductor workforce, but success depends on the widespread availability of academic training programs. In this paper, we investigate the current state of education in hardware security and HRE to identify efficient approaches for establishing effective HRE training programs. Through a systematic literature review, we uncover 13 relevant courses, including eight with accompanying academic publications. We identify common topics, threat models, key pedagogical features, and course evaluation methods. We find that most hardware security courses do not prioritize HRE, making HRE training scarce. While the predominant course structure of lectures paired with hands-on projects appears to be largely effective, we observe a lack of standardized evaluation methods and limited reliability of student self-assessment surveys. Our results suggest several possible improvements to HRE education and yield recommendations for developing new training courses. We advocate for the integration of HRE education into curriculum guidelines to meet the growing societal and industry demand for HRE experts.

Trust in digital systems depends on secure hardware, often assured through HRE. This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and Think Aloud can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.

Understanding of microchips, known as Hardware Reverse Engineering (HRE), is driven by analysts’ problem solving. This work sheds light on these hitherto poorly understood problem-solving processes. We propose a methodology addressing the problem of HRE experts being unavailable for research. We developed a training enabling students to acquire intermediate levels of HRE expertise. Besides one expert, we recruited eight top-performing students from this training for our exploratory study. All participants completed a realistic HRE task involving the removal of a copyright protection mechanism from a hardware circuit. We analyzed 2,445 log entries by applying an iterative open coding and developed a detailed hierarchical problem-solving model. Our exploration yielded insights into problem-solving strategies and revealed that two intermediates solved the task with a comparable solution time to the expert. We discuss that HRE problem solving may be a function of both expertise and cognitive abilities, and outline ideas for novel countermeasures.