Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach

TL;DR

This paper introduces a data-driven, CNN-based method for automatic detection of wire and via segmentation errors in IC images, reducing reliance on human inspection in industrial settings.

Contribution

It is the first to apply a CNN-based approach specifically for automatic error detection in IC image segmentation, achieving high accuracy.

Findings

Recall/precision of 0.92/0.93 for wire errors

Recall/precision of 0.96/0.90 for via errors

Effective adaptation of CNN techniques for industrial IC error detection

Abstract

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.