No abstract

The increasing amount of defect images in semiconductor manufacturing process imposes the demand of classifying these images in real-time. In this paper we adopt a two-step deep learning based Convolutional Neural Network (CNN) to detect and classify the defect images from clean ones. The proposed method integrates the detection and classification process into one forward step, which takes image-level feature as well as empirical defect classification rules into account. In practice, the Real-Time Automated Defect Classification (RT-ADC) system was deployed in inline production process, which brings high efficiency; better wafer analysis and root cause determination for yield enhancement.