Integrated Circuit Image Synthesis for Unsupervised Circuit Annotation via Shape Consistent Image Translation

Deep learning techniques achieve promising performance on the circuit annotation task for the hardware assurance of integrated circuits, but are reliant on large amounts of costly, labeled training data. Recently, image synthesis techniques have been explored to mitigate this reliance. However, exiting methods often lack precise pixel-level correspondence between the input image and synthetic image. When applied to IC images, this could lead to inconsistencies in the circuit structures, which then cause changes in circuit connectivity. In this paper, we propose shape consistent image translation (SCIT) to synthesize IC images that have a high pixel-level correspondence with the input images. Our experiments show that a segmentation model trained SCIT-generated images reduces the number of circuit connection errors by 56.26% compared to the second best reported technique. Our proposed SCIT also produced synthetic IC images of the highest image quality when evaluated across three IC image datasets.