A Real-Time Automated Defect Classification System Based on Two-Step Convolutional Neural Network. Issue 1 (1st September 2022)

• Record Type: Journal Article
• Authors: Wang, Sen; Yan, Shijia; Shen, Qiang; Luo, Cong; Li, Lei; Ai, Juan; Ding, Shenglan; Xia, Qing; Li, Zhi; Chen, Qilin; Li, Shilin; Dai, Hongwei
• Is Part Of: Journal of physics. Volume 2337:Issue 1(2022)
• Publication Date: 2022-09-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2337/1/012006
• Languages: English
• ISSNs: 1742-6588

Abstract

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.