Biblio

We propose an Artificial Intelligence (AI)/Deep Learning (DL)-based image analysis framework for hardware assurance of digital integrated circuits (ICs). Our aim is to examine and verify various hardware information from analyzing the Scanning Electron Microscope (SEM) images of an IC. In our proposed framework, we apply DL-based methods at all essential steps of the analysis. To the best of our knowledge, this is the first such framework that makes heavy use of DL-based methods at all essential analysis steps. Further, to reduce time and effort required in model re-training, we propose and demonstrate various automated or semi-automated training data preparation methods and demonstrate the effectiveness of using synthetic data to train a model. By applying our proposed framework to analyzing a set of SEM images of a large digital IC, we prove its efficacy. Our DL-based methods are fast, accurate, robust against noise, and can automate tasks that were previously performed mainly manually. Overall, we show that DL-based methods can largely increase the level of automation in hardware assurance of digital ICs and improve its accuracy.

Hardware Trojans are malicious modifications on integrated circuits (IC), which pose a grave threat to the security of modern military and commercial systems. Existing methods of detecting hardware Trojans are plagued by the inability of detecting all Trojans, reliance on golden chip that might not be available, high time cost, and low accuracy. In this paper, we present Golden Gates, a novel detection method designed to achieve a comparable level of accuracy to full reverse engineering, yet paying only a fraction of its cost in time. The proposed method inserts golden gate circuits (GGC) to achieve superlative accuracy in the classification of all existing gate footprints using rapid scanning electron microscopy (SEM) and backside ultra thinning. Possible attacks against GGC as well as malicious modifications on interconnect layers are discussed and addressed with secure built-in exhaustive test infrastructure. Evaluation with real SEM images demonstrate high classification accuracy and resistance to attacks of the proposed technique.