Data Augmentation With CycleGAN to Build a Classifier for Novel Defects From the Dicing Stage of Semiconductor Package Assembly

Abstract

Industry 4.0, a concept first proposed by Germany, has resulted in an increasing number of companies adopting a mass customization strategy. This strategy is widely used across various industries, enabling the production of small batches of diversified products to meet the diverse needs of customers. It encompasses the business process of providing customized goods that best fulfill individual customer requirements, thereby necessitating small-scale production of multiple products. Therefore, the product life cycle of mass customization is much shorter than other production strategies. When product line changes are frequent and customized products have high yield rates, accurately detecting potential defects from a limited number of images is a daunting challenge. If the defect identification classification model needs to maintain a certain level of identification accuracy and the model needs to be deployed quickly, it is impossible to wait until a large number of defect images are collected before deploying an accurate model for new defects. Obtaining a high-precision defect identification classification model is crucial. In this study, we employed the style transfer method of CycleGAN, which takes advantage of the unmatched training images, to successfully transfer the style of defective images from old products to defect-free images of new products. However, CycleGAN requires a large number of images for training, so this study primarily focuses on rare sample categories. We first obtained the defect mask through a semantic segmentation model and then separated the foreground defect from the wafer background using digital image processing techniques. We then copied and pasted the separated defect onto a new wafer background to generate fake defect images. Finally, a generative adversarial network architecture was used to perform image blending to make the fake defect images more natural and realistic. The effectiveness of the data augmentation method was verified through a convolutional neural network model. Through the proposed method in this study, the number of defect images in new products was successfully increased, which helps to deploy a defect identification classification model for new products quickly.

Keywords

• Automated optical inspection (AOI)
• outsourced semiconductor assembly and test (OSAT)
• artificial intelligence
• vision defects
• GAN
• image fusion