Defect Classification using Deep Learning for Hybrid Bonding Application

Abstract

In the semiconductor industry, defect detection is very important as it affects performance. In Hybrid Bonding, identifying defect types prior to bonding is critical in determining bonding performance. To overcome this challenge, we propose a solution involving Computer Vision and Deep Learning to accomplish classification of these defects with limited availability of data. With this approach, the defect identification time is reduced, thereby driving faster research and product development.