Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, we present the first data-driven automatic error detection approach targeting two types of IC segmentation errors: wire errors and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN-based approaches of image classification and image translation with additional pre-processing and post-processing techniques, we are able to achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in via error detection, respectively.

翻译：由于当前集成电路结构复杂的纳米规模结构,以及IC图像分割任务的低误容度,大多数现有的IC图像分割自动化方法要求人类专家进行视觉检查,以确保正确性,这是大规模工业应用的主要瓶颈之一,在本文件中,我们介绍了针对IC截面误差的两种类型的由数据驱动的自动误差探测方法:电线误差和误差。关于从实业界收集的IC图像数据集,我们证明,通过调整现有的CNN图像分类和图像转换方法,加上更多的预处理和后处理技术,我们能够通过误差探测分别实现对线误差探测的0.92/0.93和0.96/0.90的回溯/精确度。