  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.
