Data Augmentation of Semiconductor Defect Images for SEM-ADC

This paper proposes a method for augmenting training data targeting automatic defect classification (ADC) of semiconductor wafers using scanning electron microscope (SEM) images. In semiconductor devices, new manufacturing processes and materials are being introduced, increasing defect variations. To achieve high classification performance in ADC, it is necessary to obtain and train a large number of defect images at each manufacturing process. Therefore, we propose a new image-based data augmentation method that takes into account SEM and semiconductor characteristics. This method generates augmented images by the following three techniques: (1) image rotation/flipping considering the orientation of shadows generated in a image that includes defect topographic information, (2) shape deformation that imitates local pattern fluctuations (line edge roughness (LER)), which is one type of structural variation, and (3) generation of defect images of various sizes with physically consistent shadows considering the cast shadow caused by defects. Using a total of 773 actual device images at three processes, we confirmed that the proposed method improved ADC success rates from 51.2∼67.5% to 95.9∼97.9%. This method effectively generates pseudo images that contribute to improving ADC performance, thereby achieving sufficient classification performance required to identify the causes of defects.