2026/03/31 by Christian Gehrmann, Jonas Ricker, Simon Damm +5
Computer Science · Engineering · #Digital Media Forensic Detection #Focus (optics) #Hyperparameter #Image segmentation #Integrated Circuits and Semiconductor Failure Analysis #Integrated circuit #Leverage (statistics) #Line (geometry) #Physical Unclonable Functions (PUFs) and Hardware Security #Scalability #Segmentation #Word error rate #cs.CR #cs.CV
paper · pdf · doi:10.46586/tches.v2026.i3.122-146
openalex publication_date 2026/07/17 · openalex created_date 2026/07/18 · openalex updated_date 2026/07/29 · arxiv created 2026/08/04 · arxiv updated 2026/08/05
In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptographic applications and high-stakes scenarios. Identifying metal lines on scanning electron microscope (SEM) images of integrated circuits (ICs) is one essential step in verifying the absence of malicious circuitry in chips manufactured in untrusted environments. Due to varying manufacturing processes and technologies, such verification usually requires tuning parameters and algorithms for each target IC. Often, a machine learning model trained on images of one IC fails to accurately detect metal lines on other ICs. To address this challenge, we create SAMSEM by adapting Meta’s Segment Anything Model 2 (SAM2) to the domain of IC metal line segmentation. Specifically, we develop a multi-scale segmentation approach that can handle SEM images of varying sizes, resolutions, and magnifications. Furthermore, we deploy a topology-based loss alongside pixel-based losses to focus our segmentation on electrical connectivity rather than pixel-level accuracy. Based on a hyperparameter optimization, we then fine-tune the SAM2 model to obtain a model that generalizes across different technology nodes, manufacturing materials, sample preparation methods, and SEM imaging technologies. To this end, we leverage an unprecedented dataset of SEM images obtained from 48 metal layers across 14 different ICs. When fine-tuned on seven ICs, SAMSEM achieves an error rate as low as 0.72% when evaluated on other images from the same ICs. For the remaining seven unseen ICs, it still achieves error rates as low as 5.53%. Finally, when fine-tuned on all 14 ICs, we observe an error rate of 0.62%. Hence, SAMSEM proves to be a reliable tool that significantly advances the frontier in metal line segmentation, a key challenge in post-manufacturing IC verification.