For two decades, reverse engineering has evolved from a niche manual craft into a foundational pillar of security assurance. Yet, as the two impulse talks at the beginning of this session have shown, the field currently stands at a crossroads. While we look upon years of research and hundreds of technical methods, the practical reality is a landscape of fragmented prototypes, low reproducibility, and significant "translation friction" between academic theory and industry application.

This panel discussion moves beyond the "what" of reverse engineering to confront the "how" of its future. We bring together a diverse cohort of experts to bridge the gap between academic success, open-source mindset and contributions, and industry-grade workflows. This panel will discuss a range of ongoing challenges and their potential for tension between stakeholders, but also promising solutions, all to aim for a future where automated netlist analysis is not just a research possibility, but a reliable, scalable, and trustworthy reality.

This work presents a case study of a recent reverse-engineering analysis project. Our analysis targets IP characterization, cost modeling, and circuit extraction.

After introducing the sample’s provenance and use context, we outline a workflow that integrates sample preparation and multimodal imaging (optical microscopy and SEM). We highlight practical delayering constraints—area limits during delayering that left some regions inaccessible. We demonstrate the impact of particle contamination, film inhomogeneities, and over- and under-etching on fidelity, yield, and schedule. To manage scope and cost, we adopt a scalable hybrid pipeline combining computer-vision-assisted recognition with targeted manual digitization, producing a hierarchical circuit description suitable for architectural analysis rather than full netlist reconstruction.

Results include identification of key functional blocks, interconnect strategies, and process fingerprints, alongside an empirical cost curve for partial versus comprehensive imaging and extraction. We conclude with lessons learned on accuracy-effort trade-offs—where automation delivers value, where expert intervention is essential, and how physical realities at 40 nm shape feasibility, timelines, and budgets. The session closes with implications for chip designers on selecting techniques for future analyses, and reflections on responsible practice in reverse engineering.