  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.
