IC solder joint inspection via generator-adversarial-network based template

Automatic optical inspection is a vital part of the production process for solder joints appearance inspection in surface mounted technology assembling lines. However, IC solder joint inspection is a challenging problem because IC solder joints have extremely small sizes and no distinct appearance differences between qualified and unqualified ones. In this paper, we propose an IC solder joint inspection method via generator-adversarial-network based template. We are the first to introduce the GAN strategy into IC solder joint inspection. The method consists of GAN template generator training, offline statistical modelling and online real-time inspection. At the training stage, the GAN template generator is trained based on a designed GAN, which involves the feature maps in both of high-dimension and low-dimension spaces. Then, the binary difference image can be achieved by the input IC solder joint image and the corresponding GAN-based template. At the offline statistical modelling stage, to reduce the interferences, a pixel probability image is statistically modelled by the binary difference images corresponding to qualified IC solder joints. At the online real-time inspection stage, the potential defect pixels for the inspected IC solder joint can be shown in a defect salient image achieved by the multiplication of its corresponding binary difference image and the pixel probability image. Finally, we can accumulate the pixels in the defect salient image to distinguish the quality of the inspected IC solder joint. Experimental results show that the proposed method is superior to the state-of-the-art inspection methods with 0% omission rate and 0.15% error rate at a reasonable inspection speed of 4.32 ms per sample.