Curvature Consistent Network for Microscope Chip Image Super-Resolution

Detecting hardware Trojan (HT) from a microscope chip image (MCI) is crucial for many applications, such as financial infrastructure and transport security. It takes an inordinate cost in scanning high-resolution (HR) microscope images for HT detection. It is useful when the chip image is in low-resolution (LR), which can be acquired faster and at a lower cost than its HR counterpart. However, the lost details and noises due to the electric charge effect in LR MCIs will affect the detection performance, making the problem more challenging. In this article, we address this issue by first discussing why recovering curvature information matters for HT detection and then proposing a novel MCI super-resolution (SR) method via a curvature consistent network (CCN). It consists of a homogeneous workflow and a heterogeneous workflow, where the former learns a mapping between homogeneous images, i.e., LR and HR MCIs, and the latter learns a mapping between heterogeneous images, i.e., MCIs and curvature images. Besides, a collaborative fusion strategy is used to leverage features learned from both workflows level-by-level by recovering the HR image eventually. To mitigate the issue of lacking an MCI dataset, we construct a new benchmark consisting of realistic MCIs at different resolutions, called MCI. Experiments on MCI demonstrate that the proposed CCN outperforms representative SR methods by recovering more delicate circuit lines and yields higher HT detection performance. The dataset is available at github.com/RuiZhang97/CCN.