SEM2GDS: A Deep-Learning Based Framework To Detect Malicious Modifications In IC Layout

Overseas foundries pose potential threat to the integrity of manufactured ICs where malicious modifications, known as Hardware Trojans (HTs) may be inserted into the IC layout. To detect this, SEM images of manufactured ICs need to be compared with their original GDS images. However, existing methods either avoid direct comparison or are susceptible to errors due to the inherent differences in shapes between SEM images and GDS images. In this paper, we instead propose a Deep-Learning (DL)-based image transformation method, named SEM2GDS, which transforms a SEM image into its GDS image and produce shapes with sharp corners. This allows direct comparison between a transformed SEM image and the original GDS image for modification detection. By experiment on a set of SEM images and their corresponding GDS images, we demonstrate the efficacy of our proposed method. Our method is fast and able to achieve high detection accuracy, high f1 score, and very low False Negative Rate (FNR) of <0.02. Our method can detect real and small changes between SEM and GDS images.