Die-polygon-capturing (DPC) is a fascinating yet underexplored technique for microchip circuit extraction, rooted in the hobbyist hardware reverse engineering community. Despite its affordability, DPC has remained a manual, labor-intensive process. In this talk, we present a proof of concept for automating DPC using deep learning, bridging the gap between ingenuity and practicality in integrated circuit reverse engineering.

Our work draws on a unique dataset from the AMD 9085D microchip, an archival gem in hardware history. By applying deep learning and data augmentation, we achieved high segmentation scores, which could reduce the manual effort in DPC.

But it’s not without effort, expanding these methods to a broader range of chips requires creating a more diverse dataset. Join us as we explore the technical details, lessons learned, and broader implications of automating a technique born from the ingenuity of reverse engineering enthusiasts. If you’re curious about how deep learning can uniquely enhance microchip reverse engineering, this talk is for you.