Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield and reduced manufacturing costs. Since classical image processing systems are limited in their ability to detect novel defect patterns, and machine learning approaches often involve a tremendous amount of computational effort, this contribution introduces a novel deep neural network based hybrid approach. Unlike classical deep neural networks, a multi-stage system allows the detection and classification of the finest structures in pixel size within high-resolution imagery. Consisting of stacked hybrid convolutional neural networks (SH-CNN) and inspired by current approaches of visual attention, the realized system draws the focus over the level of detail from its structures to more task-relevant areas of interest. The results of our test environment show that the SH-CNN outperforms current approaches of learning-based automated visual inspection, whereas a distinction depending on the level of detail enables the elimination of defect patterns in earlier stages of the manufacturing process.
翻译:半导体工业的自动视觉检查旨在利用现代图像处理技术对制造缺陷进行检测和分类; 虽然尽早发现缺陷模式可以使制造链的质量控制和自动化,但制造商获益于产量的增加和制造成本的降低; 由于古典图像处理系统在发现新的缺陷模式方面能力有限,而机器学习方法往往涉及大量的计算努力,这一贡献引入了一种新型的深层次神经网络混合方法; 不同于传统的深层神经网络,多阶段系统允许在高分辨率图像中以像素大小对最佳结构进行检测和分类; 由堆叠的混合共振神经网络(SH-CNN)组成,并受当前视觉关注方法的启发,实现的系统将重点集中在其结构的详细程度到更与任务相关的领域; 我们的测试环境结果表明,SH-CNN超越了目前基于学习的自动视觉检查方法,而根据详细程度的区分有助于在制造过程的早期阶段消除缺陷模式。